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Artificial intelligence in Health Care

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• 1. VISVESVARAYA TECHNOLOGICALUNIVERSITY, BELGAUM Technical Seminar on “Artificial Intelligence in Healthcare” Presented by : Muhammed Iyas AD ( 1BT14IS009 ) Department of Information Science & Engineering BTL Institute of Technology and Management, Bangalore-99
• 2. Table of Content: • Introduction to Research Area. • Introduction to Research Topic. • Comparison with Existing System. • Applications. • Conclusion. • Reference. 4/10/2018 Artificial Intelligence in Healthcare 2
• 3. Introduction to Research Area : • Artificial Intelligence (AI) : Artificial intelligence is defined as the combination of science and the engineering on creating intelligent computer systems that are able to perform tasks without receiving any instruction directly from humans. 4/10/2018 Artificial Intelligence in Healthcare 3
• 4. Classification of AI : • Common Classification: 1. Strong Artificial Intelligence. 2. Weak Artificial Intelligence. • Classification from Arend Hintze: 1. Type 1: Reactive Machines. 2. Type 2: Limited Memory. 3. Type 3 : Theory of Mind. 4. Type 4 : Self Awareness. 4/10/2018 Artificial Intelligence in Healthcare 4
• 5. AI Technologies Used in Health care: • Machine Learning. • Machine Vision. • Natural Language Processing (NLP). • Robotics. 4/10/2018 Artificial Intelligence in Healthcare 5
• 6. Introduction to Research Topic : • Healthcare : Maintenance of health via prevention, Diagnosis and Treatment of diseases , illness, injury, and other physical and mental impairments in human beings. 4/10/2018 Artificial Intelligence in Healthcare 6
• 7. Continues…… • Existing Healthcare System : • Treatments are only based the current study. • It’s hard to find a doctor who knows you. • Lack of knowledge. • Unexpected Deaths due to human errors. 4/10/2018 Artificial Intelligence in Healthcare 7
• 8. Comparison with Existing System : • Way of Treatment. • Efficiency. • Advantages. • Challenges. 4/10/2018 Artificial Intelligence in Healthcare 8
• 9. Roles of AI in Healthcare: • Disease Prediction : • Traditionally approaches of doctors. • Use of AI technologies in this area. • Benefits. 4/10/2018 Artificial Intelligence in Healthcare 9
• 10. Example 4/10/2018 Artificial Intelligence in Healthcare 10 Figure 1 - X-RAY of a Human Hand
• 11. Continues… • Drug Manufacturing : • Old Approach. • Proposed Approach. • Benefits. 4/10/2018 Artificial Intelligence in Healthcare 11
• 12. 4/10/2018 Artificial Intelligence in Healthcare 12 Figure 2 – Robots in drug manufacturing.
• 13. Continues… • Treatment Decision : • Current Approach. • AI used Approach. • Benefits. 4/10/2018 Artificial Intelligence in Healthcare 13
• 14. 4/10/2018 Artificial Intelligence in Healthcare 14 Figure 3 – Evaluation System
• 15. 4/10/2018 Artificial Intelligence in Healthcare 15 Figure 4 – Robotical Evaluation
• 16. Continues… • Surgery : • Existing Approach. • AI used Approach. • Benefits. 4/10/2018 Artificial Intelligence in Healthcare 16
• 17. 4/10/2018 Artificial Intelligence in Healthcare 17 Figure 5 - da Vinci Surgical System.
• 18. Advantages: • Leading to advancements in healthcare treatments. • The ability to quickly and more accurately identify signs of disease. • Patients can ask medical questions and receive answers in absence of a doctor. • Reduces the treatment cost. • Makes the treatment decision faster. • Helps to reduce the human errors. 4/10/2018 Artificial Intelligence in Healthcare 18
• 19. Challenges: • Training Doctors/Patients. • Adoption. • Regulations. • Maintenance. • Security. 4/10/2018 Artificial Intelligence in Healthcare 19
• 20. Conclusion : • The primary aim of AI in healthcare is to analyze relationships between or treatment techniques and patient outcomes. • AI can achieve fast and accurate Diagnostics. • It will be very helpful to reduce the human errors as well as the cost of treatment. 4/10/2018 Artificial Intelligence in Healthcare 20
• 21. Reference : • http://searchcio.techtarget.com/definition/AI. • http://www.healthcareitnews.com/slideshow/how-ai-transforming- healthcare-and-solving-problems. • https://www.forbes.com/sites/forbestechcouncil/2018/01/30/how-ai-is- transforming-the-future-of-healthcare. • http://www.pharmtech.com/understanding-potential-artificial- intelligence-across-pharmaceutical-lifecycl’. • https://www.accenture.com/us-en/insight-artificial-intelligence- healthcare. • https://medium.com/@Unfoldlabs/the-impact-of-artificial-intelligence-in- healthcare-4bc657f129f5. 4/10/2018 Artificial Intelligence in Healthcare 21
• 22. 4/10/2018 Artificial Intelligence in Healthcare 22

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Artificial Intelligence in Healthcare

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Home / Healthy Aging / AI in healthcare: The future of patient care and health management

AI in healthcare: The future of patient care and health management

Curious about artificial intelligence? Whether you're cautious or can't wait, there is a lot to consider when AI is used in a healthcare setting.

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With the widespread media coverage in recent months, it’s likely that you’ve heard about artificial intelligence (AI) — technology that enables computers to do things that would otherwise require a human’s brain. In other words, machines can be given access to large amounts of information, and trained to solve problems, spot patterns and make recommendations. Common examples of AI in everyday life are virtual assistants like Alexa and Siri.

What you might not know is that AI has been and is being used for a variety of healthcare applications. Here’s a look at how AI can be helpful in healthcare, and what to watch for as it evolves.

What can AI technology in healthcare do for me?

A report from the National Academy of Medicine identified three potential benefits of AI in healthcare: improving outcomes for both patients and clinical teams, lowering healthcare costs, and benefitting population health.

From preventive screenings to diagnosis and treatment, AI is being used throughout the continuum of care today. Here are two examples:

Preventive care

Cancer screenings that use radiology , like a mammogram or lung cancer screening, can leverage AI to help produce results faster.

For example, in polycystic kidney disease (PKD), researchers discovered that the size of the kidneys — specifically, an attribute known as total kidney volume — correlated with how rapidly kidney function was going to decline in the future.

But assessing total kidney volume, though incredibly informative, involves analyzing dozens of kidney images, one slide after another — a laborious process that can take about 45 minutes per patient. With the innovations developed at the PKD Center at Mayo Clinic, researchers now use artificial intelligence (AI) to automate the process, generating results in a matter of seconds.

Bradley J. Erickson, M.D., Ph.D., director of Mayo Clinic’s Radiology Informatics Lab, says that AI can complete time-consuming or mundane work for radiology professionals , like tracing tumors and structures, or measuring amounts of fat and muscle. “If a computer can do that first pass, that can help us a lot,” says Dr. Erickson.

Risk assessment

In a Mayo Clinic cardiolog y study , AI successfully identified people at risk of left ventricular dysfunction, which is the medical name for a weak heart pump , even though the individuals had no noticeable symptoms. And that’s far from the only intersection of cardiology and AI.

“We have an AI model now that can incidentally say , ‘Hey, you’ve got a lot of coronary artery calcium, and you’re at high risk for a heart attack or a stroke in five or 10 years,’ ” says Bhavik Patel, M.D., M.B.A., the chief artificial intelligence officer at Mayo Clinic in Arizona.

How can AI technology advance medicine and public health?

When it comes to supporting the overall health of a population, AI can help people manage chronic illnesses themselves — think asthma, diabetes and high blood pressure — by connecting certain people with relevant screening and therapy, and reminding them to take steps in their care, such as take medication.

AI also can help promote information on disease prevention online, reaching large numbers of people quickly, and even analyze text on social media to predict outbreaks. Considering the example of a widespread public health crisis, think of how these examples might have supported people during the early stages of COVID-19. For example, a study found that internet searches for terms related to COVID-19 were correlated with actual COVID-19 cases. Here, AI could have been used to predict where an outbreak would happen, and then help officials know how to best communicate and make decisions to help stop the spread.

How can AI solutions assist in providing superior patient care?

You might think that healthcare from a computer isn’t equal to what a human can provide. That’s true in many situations, but it isn’t always the case.

Studies have shown that in some situations, AI can do a more accurate job than humans. For example, AI has done a more accurate job than current pathology methods in predicting who will survive malignant mesothelioma , which is a type of cancer that impacts the internal organs. AI is used to identify colon polyps and has been shown to improve colonoscopy accuracy and diagnose colorectal cancer as accurately as skilled endoscopists can.

In a study of a social media forum, most people asking healthcare questions preferred responses from an AI-powered chatbot over those from physicians, ranking the chatbot’s answers higher in quality and empathy. However, the researchers conducting this study emphasize that their results only suggest the value of such chatbots in answering patients’ questions, and recommend it be followed up with a more convincing study.

How can physicians use AI and machine learning in healthcare?

One of the key things that AI may be able to do to help healthcare professionals is save them time . For example:

• Keeping up with current advances. When physicians are actively participating in caring for people and other clinical duties, it can be challenging for them to keep pace with evolving technological advances that support care. AI can work with huge volumes of information — from medical journals to healthcare records — and highlight the most relevant pieces.
• Taking care of tedious work. When a healthcare professional must complete tasks like writing clinical notes or filling out forms , AI could potentially complete the task faster than traditional methods, even if revision was needed to refine the first pass AI makes.

Despite the potential for AI to save time for healthcare professionals, AI isn’t intended to replace humans . The American Medical Association commonly refers to “augmented intelligence,” which stresses the importance of AI assisting, rather than replacing, healthcare professionals. In the case of current AI applications and technology, healthcare professionals are still needed to provide:

• Clinical context for the algorithms that train AI.
• Accurate and relevant information for AI to analyze.
• Translation of AI findings to be meaningful for patients.

A helpful comparison to reiterate the collaborative nature needed between AI and humans for healthcare is that in most cases, a human pilot is still needed to fly a plane. Although technology has enabled quite a bit of automation in flying today, people are needed to make adjustments, interpret the equipment’s data, and take over in cases of emergency.

What are the drawbacks of AI in healthcare?

Despite the many exciting possibilities for AI in healthcare, there are some risks to weigh:

• If not properly trained, AI can lead to bias and discrimination. For example, if AI is trained on electronic health records, it is building only on people that can access healthcare and is perpetuating any human bias captured within the records.
• AI chatbots can generate medical advice that is misleading or false, which is why there’s a need for effectively regulating their use.

Where can AI solutions take the healthcare industry next?

As AI continues to evolve and play a more prominent role in healthcare, the need for effective regulation and use becomes more critical. That’s why Mayo Clinic is a member of Health AI Partnership, which is focused on helping healthcare organizations evaluate and implement AI effectively, equitably and safely.

In terms of the possibilities for healthcare professionals to further integrate AI, Mark D. Stegall, M.D., a transplant surgeon and researcher at Mayo Clinic in Minnesota says, “I predict AI also will become an important decision-making tool for physicians.”

Mayo Clinic hopes that AI could help create new ways to diagnose, treat, predict, prevent and cure disease. This might be achieved by:

• Selecting and matching patients with the most promising clinical trials.
• Developing and setting up remote health-monitoring devices.
• Detecting currently imperceptible conditions.
• Anticipating disease-risk years in advance.

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AI in Health Care: Applications, Benefits, and Examples

AI is changing how health care professionals provide care and how patients receive it. Learn more about what AI means for the field today – and in the coming years.

Artificial intelligence (AI) has already changed much of the world as we know it – from automating systems to improving the decisions we make and the ways we go about making them. Yet, perhaps the most impactful and personal ways AI is changing our world are within the field of health care, where it's being used to diagnose, create personalized treatment plans, and even predict patient survival rates. 

In this article, you’ll learn more about the types of AI used in health care, some of their applications and the benefits of AI within the field, as well as what the future might hold. You’ll also explore relevant jobs and online courses that can help you get started using AI for health care purposes today.  

How is AI used in health care?

Artificial Intelligence (AI) uses computers and machine processes to simulate human intelligence and perform complex automated tasks. While they seek to reflect the abilities of the human mind, AI-enabled machines are also capable of exceeding it in a number of ways, particularly by sifting through large volumes of big data efficiently in order to identify patterns, anomalies, and trends. 

Unsurprisingly, AI presents a wealth of opportunities to health care, where it can be used to enhance a variety of common medical processes – from diagnosing diseases to identifying the best treatment plans for patients facing critical illnesses like cancer. Robotic surgical equipment outfitted with AI can help surgeons better perform surgeries by decreasing their physical fluctuations and providing updated information during the operation.  

Types of AI in health care

AI is an umbrella term covering a variety of distinct, but interrelated processes. Some of the most common forms of AI used within health care include: 

Machine learning (ML) : training algorithms using data sets, such as health records, to create models capable of performing such tasks as categorizing information or predicting outcomes. 

Deep learning : A subset of machine learning that involves greater volumes of data, training times, and layers of ML algorithms to produce neural networks capable of more complex tasks. 

Neural language processing (NLP) : the use of ML to understand human language, whether it be verbal or written. In health care, NLP is used to interpret documentation, notes, reports, and published research. 

Robotic process automation (RPA) : the use of AI in computer programs to automate administrative and clinical workflows. Some health care organizations use RPA to improve the patient experience and the daily function of their facilities. 

AI applications in health care

As artificial intelligence becomes more widely adopted, so too does the number of ways the technology is being used across industries. Researchers don’t expect AI to replace health care professionals just yet. Instead, they see it as supporting and improving the work of health providers and professionals in the near future. Here are some of the most common applications of AI in the field today:

Health care analytics : ML algorithms are trained using historical data to produce insights, improve decision-making, and optimize health outcomes.

Precision medicine: AI is used to produce personalized treatment plans for patients that take into account such factors as their medical history, environmental factors, lifestyles, and genetic makeup. 

Predict diseases and illness: Using predictive models, health care professionals can determine the likelihood that someone might develop a particular condition or contract a disease. 

Interpret tests and diagnose diseases: ML models can be trained using common medical scans, like MRIs or X-rays, to interpret and diagnose such conditions as cancerous lesions.

Benefits of AI in health care

AI provides a number of benefits to the field of health care, the professionals working within it, and the patients that interact with it every day. While health care professionals can expect lower operational costs due to improved decision-making and more efficient automated services, providers can leverage the technology to design bespoke treatment plans and diagnose conditions more quickly and accurately than they could alone. Patients can expect potentially improved health outcomes and lower costs resulting from more efficient health services. 

AI jobs in health care 

Both AI and health care are growing fields that are projected to have a big impact in the coming decade. It’s little surprise, then, that AI-oriented positions are becoming increasingly common within the field of health care. 

If you’re interested in pursuing one of these careers, here are some of the positions you should consider exploring: 

1. Health informatics specialist 

2. Machine learning engineer 

3. Data scientist 

4. AI engineer

The future of AI in health care 

As with many other industries, AI is poised to change the health care landscape over the coming years. In addition to improving health facility operations, patient diagnoses, treatment plan development, and overall health outcomes, AI is also expected to help with the development and discovery of new medical cures. 

The use of artificial intelligence in health care is expected to grow significantly over the next decade. According to Grand View Research, AI in health care is forecasted to be valued at $208.2 billion in 2030, which is many times higher than its 2022 market size value of $15.4 billion [ 1 ]. 

While some research indicates that AI could lead to significant job cuts as technology automates tasks like interpreting radiologic images, others believe that this is unlikely to be the case. One 2019 research paper, for instance, asserts that actual job loss is likely to be just five percent or less over the next ten to twenty years, indicating that most job seekers have little to worry about for the foreseeable future [ 2 ]. 

Get started with Coursera 

AI is a growing and complicated field with a wealth of potential. If you’re considering a career using AI to help improve health care and maybe even solve some of the most elusive medical mysteries, then you might consider taking a relevant course through Coursera. 

Deeplearning.ai’s AI for Medicine Specialization , for example, provides practical experience applying machine learning to concrete problems in medicine like predicting patient survival rates, estimating treatment plan efficacy, and diagnosing diseases from 3D MRI brain scans. 

Article sources

Grand View Research. “ GVR Report cover Artificial Intelligence In Healthcare Market Size, Share, And Trends Analysis Report By Component (Software Solutions, Hardware, Services), By Application (Virtual Assistants, Connected Machines), By Region, And Segment Forecasts, 2022 - 2030 , https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-healthcare-marke.” Accessed November 1, 2022.

NCBI. “ The potential for artificial intelligence in healthcare , https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/.” Accessed November 1, 2022. 

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Transforming healthcare with AI: The impact on the workforce and organizations

Healthcare is one of the major success stories of our times. Medical science has improved rapidly, raising life expectancy around the world, but as longevity increases, healthcare systems face growing demand for their services, rising costs and a workforce that is struggling to meet the needs of its patients.

Demand is driven by a combination of unstoppable forces: population aging, changing patient expectations, a shift in lifestyle choices, and the never-ending cycle of innovation being but a few. Of these, the implications from an aging population stand out. By 2050, one in four people in Europe and North America will be over the age of 65—this means the health systems will have to deal with more patients with complex needs. Managing such patients is expensive and requires systems to shift from an episodic care-based philosophy to one that is much more proactive and focused on long-term care management.

Healthcare spending is simply not keeping up. Without major structural and transformational change, healthcare systems will struggle to remain sustainable. Health systems also need a larger workforce, but although the global economy could create 40 million new health-sector jobs by 2030, there is still a projected shortfall of 9.9 million physicians, nurses and midwives globally over the same period, according to the World Health Organization. 1 Global Strategy on human resources for health: Workforce 2030, World Health Organization, 2016, https://www.who.int/ hrh/resources/pub_globstrathrh-2030/en/. We need not only to attract, train and retain more healthcare professionals, but we also need to ensure their time is used where it adds most value—caring for patients.

Building on automation, artificial intelligence (AI) has the potential to revolutionize healthcare and help address some of the challenges set out above. There are several definitions of AI, but this report draws from a concise and helpful definition used by the European Parliament, “AI is the capability of a computer program to perform tasks or reasoning processes that we usually associate with intelligence in a human being.” 2 Artificial intelligence: Potential benefits and ethical considerations, European Parliament Legal Affairs briefing, Policy Department C: Citizens’ Rights and Constitutional Affairs, PE 571.380, 2016, http://www.europarl.europa.eu/RegData/ etudes/BRIE/2016/571380/IPOL_BRI(2016)571380_EN.pdf. Our working definition of AI in healthcare in this work is deliberately broad; it includes a functional continuum from the application of rules-based systems through to cutting-edge methodologies that include classic machine learning, representation learning, and deep learning. AI can lead to better care outcomes and improve the productivity and efficiency of care delivery. It can also improve the day-to-day life of healthcare practitioners, letting them spend more time looking after patients and in so doing, raise staff morale and improve retention. It can even get life-saving treatments to market faster. At the same time, questions have been raised about the impact AI could have on patients, practitioners, and health systems, and about its potential risks; there are ethical debates around how AI and the data that underpins it should be used.

This EIT Health and McKinsey & Company report aims to contribute to the debate surrounding AI in healthcare, specifically looking at how practitioners and organizations will be affected. It aims to cast light on the priorities and trade-offs for different parts of the healthcare system in Europe and beyond. The report draws on proprietary research and analyses undertaken by EIT Health and McKinsey & Company. This includes work by the McKinsey Global Institute (MGI) on the future of work in the era of automation and AI, 3 “A future that works: Automation, employment and productivity,” McKinsey Global Institute, January 2017; “Artificial intelligence: The next frontier,” McKinsey Global Institute, June 2017; “Jobs lost, jobs gained: Workforce transitions in a time of automation,” McKinsey Global Institute, December 2017; “Skill shift: Automation and the future of the workforce,” McKinsey Global Institute, May 2018; “‘Tech for good’: Using technology to smooth disruption and improve well-being,” McKinsey Global Institute, May 2019. See https://www.mckinsey.com/featured-insights/future-of-work . analyzing the impact on healthcare practitioners in Europe; a series of one-to-one interviews with 62 healthcare and other leaders with experience in AI and digital health, and an online survey of 175 healthcare professionals, healthcare investors, and AI startup founders and other executives. AI in healthcare being a fast-moving field, the report provides a unique vantage point from the frontline of healthcare delivery and innovation today and the latest view from a wide array of stakeholders on AI’s potential, the real state of play today, and what is holding us back.

Last, to highlight where AI is already having an impact in healthcare, the report also looks at detailed examples of existing AI solutions in six core areas where AI has a direct impact on the patient and three areas of the healthcare value chain that could benefit from further scaling of AI (Exhibit 1).

In doing so, the report provides a unique contribution to the debate on the impact of AI in healthcare in four ways: 1) decision makers’ view of the state-of-play in this fast-moving field, where developments from just 12 months ago are considered “old news”; 2) a robust new methodology to evaluate the impact of automation and AI on specific skills and activities in healthcare in Europe; 3) a substantial review of use cases that illustrate the potential that AI is already on track to deliver; and 4) a unique view from the frontline, hearing from healthcare professionals, investors and startup executives on where the real potential, opportunities and barriers lie.

The report does not attempt to cover all facets of this complex issue, in particular the ethics of AI or managing AI-related risks, but does reflect the efforts on this important topic led by EIT Health and other EU institutions. Equally, while it acknowledges the potential disruptive impact of personalization on both healthcare delivery and healthcare innovation in the future (e.g., in R&D), the report focuses primarily on the impact of AI on healthcare professionals and organizations, based on the use cases available today.

Last, AI is in its infancy and its long-term implications are uncertain. Future applications of AI in healthcare delivery, in the approach to innovation and in how each of us thinks about our health, may be transformative. We can imagine a future in which population-level data from wearables and implants change our understanding of human biology and of how medicines work, enabling personalized and real-time treatment for all. This report focuses on what is real today and what will enable innovation and adoption tomorrow, rather than exploring the long-term future of personalized medicine. Faced with the uncertainty of the eventual scope of application of emerging technologies, some short-term opportunities are clear, as are steps that will enable health providers and systems to bring benefits from innovation in AI to the populations they serve more rapidly.

AI in healthcare today

More data, better data, more connected data.

What do we mean by AI in healthcare? In this report we include applications that affect care delivery, including both how existing tasks are performed and how they are disrupted by changing healthcare needs or the processes required to address them. We also include applications that enhance and improve healthcare delivery, from day-to-day operational improvement in healthcare organizations to population-health management and the world of healthcare innovation. It’s a broad definition that covers natural language processing (NLP), image analysis, and predictive analytics based on machine learning. As such, it illustrates a spectrum of AI solutions, where encoding clinical guidelines or existing clinical protocols through a rules-based system often provides a starting point, which then can be augmented by models that learn from data.

AI is now top-of-mind for healthcare decision makers, governments, investors and innovators, and the European Union itself. An increasing number of governments have set out aspirations for AI in healthcare, in countries as diverse as Finland, Germany, the United Kingdom, Israel, China, and the United States and many are investing heavily in AI-related research. The private sector continues to play a significant role, with venture capital (VC) funding for the top 50 firms in healthcare-related AI reaching $8.5 billion, and big tech firms, startups, pharmaceutical and medical-devices firms and health insurers, all engaging with the nascent AI healthcare ecosystem.

Geographically, the dynamics of AI growth are shifting. The United States still dominates the list of firms with highest VC funding in healthcare AI to date, and has the most completed AI-related healthcare research studies and trials. But the fastest growth is emerging in Asia, especially China, where leading domestic conglomerates and tech players have consumer-focused healthcare AI offerings and Ping An’s Good Doctor, the leading online health-management platform already lists more than 300 million users. Europe, meanwhile, benefits from the vast troves of health data collected in national health systems and has significant strengths in terms of the number of research studies, established clusters of innovation and pan-European collaborations, a pan-European approach to core aspects of AI (e.g., ethics, privacy, “trustworthy AI”) and an emerging strategy on how to ensure the “EU way” for AI helps deliver the advantages for AI to its population. Yet, at the same time, valuable data sets are not linked, with critical data-governance, access, and security issues still needing to be clarified, delaying further adoption. European investment and research in AI are strong when grouped together but fragmented at the country or regional level. Overall, there is a significant opportunity for EU health systems, but AI’s full potential remains to be explored and the impact on the ground remains limited. A surprising 44 percent of the healthcare professionals we surveyed—and these were professionals chosen based on their engagement with healthcare innovation—had never been involved in the development or deployment of an AI solution in their organization.

Growing number of use cases

While there are widespread questions on what is real in AI in healthcare today, this report looked at 23 applications in use today and provides case studies of 14 applications already in use. These illustrate the full range of areas where AI can have impact: from apps that help patients manage their care themselves, to online symptom checkers and e-triage AI tools, to virtual agents that can carry out tasks in hospitals, to a bionic pancreas to help patients with diabetes. Some help improve healthcare operations by optimizing scheduling or bed management, others improve population health by predicting the risk of hospital admission or helping detect specific cancers early enabling intervention that can lead to better survival rates; and others even help optimize healthcare R&D and pharmacovigilance. The scale of many solutions remains small, but their increasing adoption at the health-system level indicates the pace of change is accelerating. In most cases, the question is less whether AI can have impact, and more how to increase the potential for impact and, crucially, how to do so while improving the user experience and increasing user adoption.

Three phases of scaling AI in healthcare

We are in the very early days of our understanding of AI and its full potential in healthcare, in particular with regards to the impact of AI on personalization. Nevertheless, interviewees and survey respondents conclude that over time we could expect to see three phases of scaling AI in healthcare, looking at solutions already available and the pipeline of ideas.

First, solutions are likely to address the low-hanging fruit of routine, repetitive and largely administrative tasks, which absorb significant time of doctors and nurses, optimizing healthcare operations and increasing adoption. In this first phase, we would also include AI applications based on imaging, which are already in use in specialties such as radiology, pathology, and ophthalmology.

In the second phase, we expect more AI solutions that support the shift from hospital-based to home-based care, such as remote monitoring, AI-powered alerting systems, or virtual assistants, as patients take increasing ownership of their care. This phase could also include a broader use of NLP solutions in the hospital and home setting, and more use of AI in a broader number of specialties, such as oncology, cardiology, or neurology, where advances are already being made. This will require AI to be embedded more extensively in clinical workflows, through the intensive engagement of professional bodies and providers. It will also require well designed and integrated solutions to use existing technologies effectively in new contexts. This scaling up of AI deployment would be fuelled by a combination of technological advancements (e.g., in deep learning, NLP, connectivity etc.) and cultural change and capability building within organizations.

In the third phase, we would expect to see more AI solutions in clinical practice based on evidence from clinical trials, with increasing focus on improved and scaled clinical decision-support (CDS) tools in a sector that has learned lessons from earlier attempts to introduce such tools into clinical practice and has adapted its mind-set, culture and skills. Ultimately respondents would expect to see AI as an integral part of the healthcare value chain, from how we learn, to how we investigate and deliver care, to how we improve the health of populations. Important preconditions for AI to deliver its full potential in European healthcare will be the integration of broader data sets across organizations, strong governance to continuously improve data quality, and greater confidence from organizations, practitioners and patients in both the AI solutions and the ability to manage the related risks.

How will AI change the healthcare workforce?

The MGI has studied how automation and AI are likely to affect the future of work. It concludes that automation will affect most jobs across sectors, but the degree varies significantly, and healthcare is one of the sectors with the lowest overall potential for automation—only 35 percent of time spent is potentially automatable and this varies by type of occupation. The potential for automation is different to the likelihood of adoption.

The analysis uses a midpoint scenario, which estimates that 15 percent of current work hours in healthcare are expected to be automated. Exhibit 2 shows the share of hours currently worked that could be freed up by automation by 2030 for a wide range of healthcare occupations in selected European countries. This does not reflect the potential for further disruption through other factors, such as personalization, that may revolutionize healthcare by focusing on a “segment of one.”

How will automation and AI affect the number of jobs in healthcare? The reality is that the European healthcare sector faces a significant workforce gap that is only expected to widen. The World Health Organization estimates overall demand for healthcare workers to rise to 18.2 million across Europe by 2030 and, as an example, states that the current supply of 8.6 million nurses, midwives, and healthcare assistants across Europe will not meet current or projected future need. 4 Global strategy on human resources for health: Workforce 2030, World Health Organization, https://www.who.int/hrh/ resources/pub_globstrathrh-2030/en/, 2016; Healthcare personnel statistics - nursing and caring professionals, Eurostat, 2019, https://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_personnel_statistics_-_nursing_and_ caring_professionals#Healthcare_personnel. The MGI analysis of the demand for specific types of healthcare activities suggests significant increases in the need for specific professionals, such as licensed practical and vocational nurses, home health aides, and others, who are core to the day-to-day delivery of care to European citizens. It highlights that automation could, in fact, alleviate workforce shortages in healthcare, as demand for occupations is set to increase. For example, a 39 percent increase in all nursing occupations is expected by 2030, even allowing for the fact that approximately 10 percent of nursing activities could be freed up by automation.

The impact on the workforce will be much more than jobs lost or gained—the work itself will change. At the heart of any change is the opportunity to refocus on and improve patient care. AI can help remove or minimize time spent on routine, administrative tasks, which can take up to 70 percent of a healthcare practitioner’s time. A recurring theme in interviews was that this type of AI role would not just be uncontroversial but would top of most people’s wish list and would speed up adoption. AI can go further. It can augment a range of clinical activities and help healthcare practitioners access information that can lead to better patient outcomes and higher quality of care. It can improve the speed and accuracy in use of diagnostics, give practitioners faster and easier access to more knowledge, and enable remote monitoring and patient empowerment through self-care. This will all require bringing new activities and skills into the sector, and it will change healthcare education—shifting the focus away from memorizing facts and moving to innovation, entrepreneurship, continuous learning, and multidisciplinary working. The biggest leap of all will be the need to embed digital and AI skills within healthcare organizations—not only for physicians to change the nature of consultations, but for all frontline staff to integrate AI into their workflow. This is a significant change in organizational culture and capabilities, and one that will necessitate parallel action from practitioners, organizations and systems all working together.

The final effect on the workforce will be the introduction of new professionals. Multiple roles will emerge at the intersection of medical and data-science expertise. For example, medical leaders will have to shape clinically meaningful and explainable AI that contains the insights and information to support decisions and deepen healthcare professionals’ understanding of their patients. Clinical engagement will also be required in product leadership, in order to determine the contribution of AI-based decision-support systems within broader clinical protocols. Designers specializing in human-machine interactions on clinical decision making will help create new workflows that integrate AI. Data architects will be critical in defining how to record, store and structure clinical data so that algorithms can deliver insights, while leaders in data governance and data ethics will also play vital roles. In other data-rich areas, such as genomics, new professionals would include ‘hybrid’ roles, such as clinical bioinformaticians, specialists in genomic medicine, and genomic counsellors. Institutions will have to develop teams with expertise in partnering with, procuring, and implementing AI products that have been developed or pioneered by other institutions. Orchestrating the introduction of new specializations coming from data science and engineering within healthcare delivery will become a critical skill in itself. There will be an urgent need for health systems to attract and retain such scarce and valuable talent, for example, by developing flexible and exciting career paths and clear routes to leadership roles.

What needs to change to encourage the introduction and scaling of AI in healthcare?

The strides made in the field of AI in healthcare have been momentous. Moving to a world in which AI can deliver significant, consistent, and global improvements in care will be more challenging.

Of course, AI is not a panacea for healthcare systems, and it comes with strings attached. The analyses in this report and the latest views from stakeholders and frontline staff reveal a set of themes that all players in the healthcare ecosystem will need to address:

• Working together to deliver quality AI in healthcare. Quality came up in our interviews time and again, especially issues around the poor choice of use cases, AI design and ease of use, the quality and performance of algorithms, and the robustness and completeness of underlying data. The lack of multidisciplinary development and early involvement of healthcare staff, and limited iteration by joint AI and healthcare teams were cited as major barriers to addressing quality issues early on and adopting solutions at scale. The survey revealed this is driven by both sides: only 14 percent of startup executives felt that the input of healthcare professionals was critical in the early design phase; while the healthcare professionals saw the private sector’s role in areas such as aggregating or analyzing data, providing a secure space for data lakes, or helping upskill healthcare staff as minimal or nonexistent. One problem AI solutions face is building the clinical evidence of quality and effectiveness. While startups are interested in scaling solutions fast, healthcare practitioners must have proof that any new idea will “do no harm” before it comes anywhere near a patient. Practitioners also want to understand how it works, where the underlying data come from and what biases might be embedded in the algorithms, so are interested in going past the concept of AI as a “black box” to understand what underpins it. Transparency and collaboration between innovators and practitioners will be key in scaling AI in European healthcare. User-centric design is another essential component of a quality product. Design should have the end user at its heart. This means AI should fit seamlessly with the workflow of decision makers and by being used, it will be improved. Many interviewees agreed that if AI design delivers value to end users, those users are more likely to pay attention to the quality of data they contribute, thereby improving the AI and creating a virtuous circle. Finally, AI research needs to heavily emphasize explainable, causal, and ethical AI, which could be a key driver of adoption.
• Rethinking education and skills. We have already touched on the importance of digital skills—these are not part of most practitioners’ arsenal today. AI in healthcare will require leaders well-versed in both biomedical and data science. There have been recent moves to train students in the science where medicine, biology, and informatics meet through joint degrees, though this is less prevalent in Europe. More broadly, skills such as basic digital literacy, the fundamentals of genomics, AI, and machine learning need to become mainstream for all practitioners, supplemented by critical-thinking skills and the development of a continuous-learning mind-set. Alongside upgrading clinical training, healthcare systems need to think about the existing workforce and provide ongoing learning, while practitioners need the time and incentive to continue learning.
• Strengthening data quality, governance, security and interoperability. Both interviewees and survey respondents emphasized that data access, quality, and availability were potential roadblocks. The data challenge breaks down into digitizing health to generate the data, collecting the data, and setting up the governance around data management. MGI analyses show that healthcare is among the least digitized sectors in Europe, lagging behind in digital business processes, digital spend per worker, digital capital deepening, and the digitization of work and processes. It is critical to get the basic digitization of systems and data in place before embarking on AI deployments—not least because the frustrations staff have with basic digitization could spill over to the wider introduction of AI. In addition, as more healthcare is delivered using new digital technologies, public concerns about how healthcare data are used have grown. Healthcare organizations should have robust and compliant data-sharing policies that support the improvements in care that AI offers while providing the right safeguards in a cost-efficient way. Physicians we interviewed emphasized that, given the volume of data required for AI, a poorly thought out process of anonymization could be a major cost, making diagnostic algorithms prohibitively expensive. Interviewees also emphasized, however, that both healthcare as a sector and Europe as a region have significant advantages. First, both healthcare organizations and health systems are used to dealing with sensitive data through well-structured data governance and risk-management processes. In some cases, healthcare could lead the way for other sectors seeking to put such measures in place. Secondly, Europe benefits from national health systems with extensive data sets, often shared within integrated care systems, offering a set of systems and processes to build on that could also serve as examples to other regions. The final data challenge is getting data sets to talk to each other. Policymakers, funding bodies and nonprofit organizations need to support efforts to sufficiently anonymize and link data and, where sensible, to build databases that can be accessed by stakeholders with the appropriate safeguards. In order to make the most of the rich data that is available, healthcare systems need an interconnected data infrastructure. This is an area where Europe, as mentioned, could have a significant advantage, in terms of its extensive national data sets and its networks of innovations clusters or hubs and pan-European collaborations with academia and industry, providing a prototype for the creation of centers of excellence for AI in healthcare.
• Managing change. Managing change while introducing AI is no different to managing change in complex institutions more broadly, but for healthcare, clinical leadership is key, as is being open to identifying the right use cases that support rather than antagonize practitioners and truly augment rather than substitute their ability to deliver the best possible care to their patients. This could include prioritizing solutions that focus on reducing the time people spend on routine administrative tasks, rather than those that seek to act as virtual assistants who interact directly with patients, or CDS tools that facilitate activities physicians see as core to their professional role, i.e., the clinical diagnosis. Healthcare providers also need to be transparent about the benefits and risks of AI and work with staff to harness the collective energy of their teams and capitalize on the opportunities AI can bring. It may not be a rapid process, but it soon becomes increasingly rewarding for practitioners and is an important part of the overall adoption process.
• Investing in new talent and creating new roles. Healthcare organizations need to consider how they will develop and recruit the new roles that will be critical to the successful introduction and adoption of AI, such as data scientists or data engineers. Demand for such skills is heating up across industries and the competition for talent will be fierce, but many young data professionals find a true vocation in healthcare and its mission and are excited about the potential of digital health and AI. Developing flexible, agile models to attract and retain such talent will be a key part of these organizations’ people strategy.
• Working at scale. The lessons from public- and private-sector actors aiming to develop AI in healthcare to date suggest that scale matters—largely due to the resources needed to develop robust AI solutions or make them cost-efficient. Not every hospital will be able to afford to attract new AI talent, or have access to enough data to make algorithms meaningful. Smaller organizations can benefit from working in innovation clusters that bring together AI, digital health, biomedical research, translational research or other relevant fields. Larger organizations can develop into centres of excellence that pave the way for regional and public-private collaborations to scale AI in European healthcare.
• Regulation, policymaking and liability, and managing risk. Responsibility for AI solutions—both clinical and technical—is split today between healthcare organizations and their staff. Interviewees emphasized the importance of clarifying whether AI will be regulated as a product or as a tool that supports decision making, and of introducing a consistent regulatory approach for AI similar to that provided by the European Medicines Agency (EMA) on medicines or by national authorities on medical devices. Another issue to be clarified across Europe is the extent to which patients’ access to some AI tools needs to be regulated or restricted to prescription. The issue of liability and risk management is a particular challenge. Patient safety is paramount, but healthcare providers also have to think about the professional accountability of their clinicians, as well the protection of their organizations from reputational, legal or financial risk. Healthcare lawyers interviewed in this report were clear that accountability ultimately rests with the clinician under current laws. Innovators are also proactively addressing related risks. Many are putting new processes in place and ensuring a “compliance by design” approach is at the core of product development.
• Funding. The reimbursement of medicines and medical devices across Europe is complicated and is even less clear when it comes to AI solutions. The responsibility for decisions on the reimbursement of a medicine or device rests with national and local payor organizations depending on the country, and this decision usually covers what will be reimbursed and at what price. Clear criteria for the potential reimbursement of AI applications will be crucial for its adoption at scale, alongside creative funding models that ensure the benefits are shared across organizations.

What this could mean for healthcare organizations

European healthcare providers need to assess what their distinctive role or contribution can be in introducing or scaling AI in healthcare. They need to take stock of their capabilities, level of digitization, availability and quality of data, resources and skills and then define their level of ambition for AI as it fits with their strategic goals. They should also define the enablers they need to put in place. These could include creating an AI ecosystem through partnerships to codevelop the right solutions for their population; codeveloping a compelling narrative on AI with patients and practitioners; defining and developing the right use cases jointly with end users; defining and addressing skill gaps in digital literacy for their staff; refining their value proposition for AI talent; addressing data-quality, access, governance, and interoperability issues; and shaping a culture of entrepreneurship. All these themes were echoed by the healthcare professionals in the survey, who listed the top three things healthcare organizations could do, as: bringing together multidisciplinary teams with the right skills, improving the quality and robustness of data and identifying the right use cases.

What this could mean for health systems

European health systems can play a more fundamental role in catalyzing the introduction and scaleup of AI. Key actions they could take include:

• Develop a regional or national AI strategy for healthcare, defining a medium- and longer-term vision and goals, specific initiatives, resources and performance indicators. Define use cases to support through targeted funding and incentives to enable scaling of AI solutions across the system; ensure these deliver against both clinical and operational outcomes.
• Set standards for digitization, data quality and completeness, data access, governance, risk management, security and sharing, and system interoperability; incentivize adherence to standards through a combination of performance and financial incentives.
• Redesign workforce planning and clinical-education processes to address the needs of both future healthcare and AI-focused professionals; and invest upfront in upskilling frontline staff and designing lifelong-learning programs through continuing professional development and degrees or diplomas for healthcare professionals.
• Provide incentives and guidance for healthcare organizations to collaborate in centers of excellence/clusters of innovation at the regional or national level.
• Address AI regulation, liability and funding issues, creating the right environment for appropriate, safe and effective AI solutions to be adopted but minimizing the risk to practitioners.
• Ensure this is reflected in funding and reimbursement mechanisms for innovation in healthcare—the number one priority for survey respondents from health systems, alongside simplifying data-governance and data-sharing processes.

What this could mean for Europe

Our early analyses of levels of VC investment and AI-related clinical trials, as well as the number of companies and M&A deals in digital health and AI, show this is a fast-moving market where Europe, as a group of countries, plays a growing role internationally alongside the United States and China. The scale needed to effectively roll out AI in healthcare may place a toll on smaller EU Member States but could be easily reached through collaborations across Europe. Interviewees and survey respondents were clear on the potential impact of the European Union in helping deliver the promise of AI, faster and at a greater scale for Europe’s population. They highlighted the following specific strands of work that could be considered:

• Consolidating funding against strategic AI priorities. Defining a few concrete priorities for AI in European healthcare and consolidating funding to support them strategically could provide a much-needed stimulus to fast-track promising developments in AI for healthcare.
• Creating a level playing field across Europe. Common standards on data, regulation, access, privacy, or interoperability, and shared requirements on data exchange, would enable innovators to scale AI solutions cost-effectively, while focusing their energies on entrepreneurship. It would also enable patients, practitioners, and health systems to develop the same confidence in new AI solutions that they have now in new medicines and medical devices that have undergone European approval.
• Clarifying key aspects of regulation around product approval, accountability, governance and litigation. The European Union can help remove barriers to adopting AI at the national and local level, providing clarity on approval processes across Europe, potentially creating regulatory centers of excellence for AI regulation, and setting expectations on accountability and liability.
• Encouraging and supporting the creation of centers of excellence for AI in healthcare. This can help consolidate scarce AI talent in high-profile and agile networks that can move quickly from design to implementation and spearhead the introduction of new capabilities in national health systems. These centers of excellence would also lead the way in adopting and implementing technologies and approaches developed elsewhere. Indeed, their expertise in applying approaches to improve care will be as critical as their expertise in developing those approaches in the first place. They can also ensure that talent creation and continuous learning are prioritized and enhanced at the European level.
• Playing an active role in AI. This will ensure that the thoughtful European approach to ethics, health data and patient confidentiality shapes the AI sector, in the same way that GDPR (General Data Protection Regulation) has for privacy protection.

Overall, this report highlights the excitement of Europe-wide stakeholders, healthcare professionals, investors, and innovators about the impact of AI on European healthcare, and about the thoughtful approach taken across Europe to ensure this delivers ethical and trustworthy AI. It also highlights that this is only the latest view across Europe and internationally—speed is of the essence if Europe is to continue playing a leading role in shaping the AI of the future to deliver its true potential to European health systems and their patients.

AI in Healthcare

Ai in healthcare – what healthcare leaders need to know… now, presentation background.

Hardly a day goes by when there is not a story in the news about artificial intelligence and its impact on society. New advances in technology and applications—both exciting and scary—seem to be occurring exponentially. There is now evidence that AI can learn on its own and that it can “make things up” or even hallucinate. GPT is enabling technology that is increasingly like a human. Computer-brain interfaces can physically connect the mind to the computer.

How does the healthcare executive stay on top of the science and make knowledgeable decisions? Or do we just let AI take over the world? Might your new doctor be an AI hologram?

Dr. Steve Berkowitz  will review the basic models and concepts of AI such as processing speed, machine learning and networks, with particular emphasis on their applications in healthcare. He will discuss special interest topics such as GPT and other conversational AI in healthcare, along with brain-computer interfaces, emergent properties, and the concept of singularity. He will discuss the past, present, and future impact of AI on healthcare systems, patients, and providers.

Finally, with the “Pause” letter on GPT technology , Dr. Berkowitz will discuss the moral implications of AI both in and out of healthcare, and the role of future regulations and compliance.

“AI can learn on its own, and it can sometimes make things up.”

“Might your new doctor be an AI hologram?”

AI in Healthcare Presentation Objectives

• Review the basic models and concepts of artificial intelligence.
• Discuss the implications of specific topics such as emergent properties, hallucinations, brain-computer interfaces, and how they can contribute to AI singularity.
• Review the impact of GPT technologies, particularly how they would affect the providers and the hospital.
• Discuss the overall present and future impact of AI on the basic delivery of patient care.
• Discuss the social/moral issues of AI and potential need for regulation.

HEALTHCARE KEYNOTE SPEAKER

Book dr. steve berkowitz.

Recognized as a leading expert in healthcare quality, administration, and AI, Dr. Steve Berkowitz speaks to healthcare organizations all over the country.

Interested in a particular presentation topic? Reach out today.

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Presentation matters for AI-generated clinical advice

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If mistakes are made in clinical settings, patients suffer. Artificial intelligence (AI) generally — and large language models specifically — are increasingly used in health settings, but the way that physicians use AI tools in this high-stakes environment depends on how information is delivered. AI toolmakers have a responsibility to present information in a way that minimizes harm.

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Acknowledgements

M.G. is a CIFAR AI Chair, CIFAR Azrieli Global Scholar, Herman L. F. von Helmholtz Career Development Professor, and JameelClinic Affiliate, and acknowledges support from these programmes.

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Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

Taridzo chomutare.

1 Norwegian Centre for E-Health Research, 9019 Tromsø, Norway

Miguel Tejedor

Therese olsen svenning, luis marco-ruiz, maryam tayefi, karianne lind, fred godtliebsen.

2 Department of Mathematics and Statistics, Faculty of Science and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway

3 Institute for Health and Society, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway

Leila Ismail

4 Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates

5 National Water and Energy Center, United Arab Emirates University, Al Ain 15551, United Arab Emirates

6 School of Computing and Information Systems, Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC 3010, Australia

Alexandra Makhlysheva

Phuong dinh ngo, associated data.

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention’s generalizability and interoperability with existing systems, as well as the inner settings’ data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.

1. Introduction

Nowadays, artificial intelligence (AI) has become ubiquitous, and much more advanced and user-friendly than it was two decades ago. In many respects, AI has become reliable and permeates many aspects of our daily lives, such as face and speech recognition apps. Yet, only recently have we seen a corresponding rate of adoption in the healthcare services. AI systems can emerge as a smart solution to reduce clinical staff workload in a world with increasingly saturated healthcare systems. AI is different from simple technology interventions in the sense that AI does not just manage data, but it provides suggestions and recommendations directly shaping the clinical decision process [ 1 , 2 ].

There exists a wide body of literature on the barriers to and facilitators of implementing AI in healthcare [ 3 , 4 , 5 ]. However, much of what we know about these barriers and facilitators comes from anecdotal evidence [ 6 ], narrative commentaries [ 7 ] and reviews [ 8 , 9 , 10 , 11 ], mostly without any empirical support or sound theoretical basis. As a result, the determinants of AI implementation success in healthcare are still poorly understood [ 12 ]. We lack a complete overview of all the factors that are relevant to implementing AI in clinical settings. In this study, we turn to implementation science [ 13 ] to analyze the facilitators and barriers, based on accounts from existing implementations.

Implementation science is a fairly new field, whose emerging theories, models and frameworks have the potential to inform our understanding of AI implementation in a more widely accessible and systematic way. This multidisciplinary approach, combining AI and implementation science, transcends the traditional boundaries of each of the fields. Blending these two disparate, yet complementary, fields is key to our understanding of AI implementation in healthcare. However, there is a need to reconcile the methodological differences and conflicting domain-specific jargon. In the next two subsections, we explore the fundamental aspects of each of these two fields.

1.1. Artificial Intelligence

AI is not a new concept, but renewed interest in the field is widely attributed to the increasing abundance of digital data and the advancements in data analytic approaches. AI comprises many different areas that range from logic-based models to machine learning (ML). Logic-based models [ 14 ] have been successfully used in areas such as biomedical ontologies management (e.g., SNOMED-CT automatic concept classification [ 15 ]) and decision support (e.g., SAGUE, Arden syntax, GLIF, etc. [ 16 ]). Conversely, ML has had a less prominent role, partially due to the lack of health data availability for training data-driven algorithms. Data-driven methods have the capacity to unveil patterns in data that otherwise would remain hidden [ 17 ]. They stand a comparatively better chance at dealing with subpopulations, where one clinical guideline may not suffice to provide the optimal treatment (e.g., multimorbid patients).

In the scope of this study, we refer to AI as systems that are used to solve healthcare problems of interest and are powered by ML. Witten et al. [ 18 ] define ML as ”a family of statistical and mathematical modeling techniques that use a variety of approaches to automatically learn and improve the prediction of a target state, without explicit programming”. This definition precludes most expert systems and other basic knowledge-based AI systems that use simple rule-based processes or Boolean rules.

1.2. Implementation Science

In a seminal paper, Eccles and Mittman [ 13 ] define implementation science as “…the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practice into routine practice…”. In contrast, AI, comprised mostly of computing sciences, defines implementation as generally referring to development of software components according to a specification, for example, implementing an algorithm. To the extent that these two fields define implementation in significantly different ways, their focus as academic fields will also diverge markedly. Computing sciences focus more on developing artefacts rather than systematically studying how the artefacts are put into routine use. This lack of shared meaning will inevitably have serious consequences for search strategies to find relevant articles in academic databases. For the purpose of discussion in this study, we use the definition of implementation from implementation science.

1.3. Pilot Study vs. Implementation Trial

In the context of screening for implementation trials, there is a thin line between a pilot study and an implementation trial. Pilot studies and feasibility studies are necessary components in the path to implementation. Curran et al. [ 19 ] describe a progressive path from efficacy studies, followed by effectiveness studies and then proceeding to implementation research. Pearson et al. [ 20 ] distinguish between studies conducted for testing effectiveness and studies intended to evaluate implementation strategies, using three conceptualizations named Hybrid Type 1, Type 2 and Type 3. These conceptualizations are based on Curran et al.’s [ 19 ] work on combining both effectiveness studies and implementation science elements. Major distinctions are made between the purpose of the study and the methods used. The primary purpose of Hybrid Type 1 is for testing the clinical or public health effectiveness of an intervention. Hybrid Type 2 considers both the clinical effectiveness and evaluation of an implementation strategy. The primary goal of Hybrid Type 3 is to evaluate the effectiveness of the implementation strategies, with a secondary goal to observe other data such as health outcomes. In the current study, we focus on studies evaluating implementation strategies (Hybrid Type 2 and 3), where a full implementation already exists or where the organization is committed to a full roll-out, and the smaller implementation trial forms part of a risk minimization strategy. Thus, pilot studies that fall under Hybrid Type 1 are excluded.

1.4. Objectives

The goal of this scoping review is to characterize the barriers and facilitators influencing the implementation of ML methods in the healthcare setting. This study differs from the existing reviews in at least two major ways. First, whereas the existing reports on barriers and facilitators are fragmented, this study analyzes these barriers and facilitators in a more systematic and theoretic way, which allows us to identify reporting problems and knowledge gaps. Second, the existing reviews do not discriminate based on the phases of implementation. Therefore, most of the studies include algorithm development, efficacy and effectiveness studies. The current study, on the other hand, focuses on empirical observations from the late phases of implementation and roll-out.

2. Methodology

This scoping review follows standard reporting, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews [ 21 ] (see Additional file S4 —PRISMA checklist). A scoping review is an appropriate methodology for exploring new areas of research [ 22 ]. There are only a few implementations, and reviewing auxiliary information sources, such as reports or websites of the implementation, adds value to our overall understanding of the implementation context. The authors are a multidisciplinary team of statisticians, data scientists, computer scientists and clinicians. The authors have had experience in the implementation of data-driven ML methods and their performance evaluation.

2.1. Protocol

Since this review is scoping in nature and required an additional search phase, no protocol was published in advance. However, the data extraction form was designed before starting the search.

2.2. Eligibility Criteria

The screening goal was to exclude articles that do not study actual or real-world implementations. To identify and understand the barriers and facilitators, based on empirical observations and real experiences with production systems, we included implementation trials and excluded early pilot testing of algorithms. Table 1 shows the eligibility criteria based on the population, intervention, comparator, outcomes and study type (PICOS). We searched for papers published between 2015 and 2021. Only publications in English were included.

Study eligibility criteria based on PICOS.

2.3. Information Sources and Selecting Sources of Evidence

The databases and indexes that we searched included PubMed, IEEE, ACM, Google Scholar, and the Web of Science. These sources represent the major indices of scientific articles related to both AI–ML and the healthcare sciences.

In addition to scientific publication databases, we used other sources of information, such as the database of FDA-approved AI systems [ 23 ]. We performed an additional Google search on the Internet to gain a better understanding of both the functions of the system and the implementation context. We used website information and any available reports relevant to the specific implementation. These auxiliary information sources are appropriate for use in a scoping review and helped us screen the studies. However, the data extraction was only based on scientific articles.

2.4. Search Query and Two-Phase Search

As a multidisciplinary team of researchers, we knew about the conflicting definitions of implementation. However, we could not anticipate the extent of the problem or how it would affect our search results. We defined an iterative search process with two phases. In the first phase, we searched only the title with terms such as “implement*” and “practice”. Through a limited screening of the title and abstract, we quickly realized that many potentially relevant papers were missed, and most of the papers were about implementing algorithms. In phase two of the search, we had to define our search more broadly; include both title and abstract, and more synonyms. This iterative approach to a search strategy is supported by the literature [ 24 ].

We identified a broad spectrum of studies, and, given the lack of a unified vocabulary for indexing relevant articles, we had to make a subjective judgement regarding where a study fell on a continuum: (i) algorithm implementation, (ii) efficacy, effectiveness or algorithm validation, (iii) implementation trial, or (iv) full implementation. An overwhelming majority of the search hits fell within (i) and (ii). Only the studies identified as class (iii) or (iv) were included in this study.

The basic structure of the search query was «Artificial intelligence AND implementation AND healthcare». Synonyms and terms related to AI were then added using the logical disjunction operator (OR). The initial abstract screening was done using Rayyan [ 25 ]. All the search strings are available in [ Additional File S1 —search string]. An example of the search in PubMed was as follows:

(«machine learning»[Title/Abstract] OR machine learning[mesh] OR «artificial intelligence»[Title/Abstract] OR artificial intelligence[mesh] OR «deep learning»[Title/Abstract] OR deep learning[mesh] OR «neural network»[Title/Abstract] OR «image analysis»[Title/Abstract] OR «deep neural networks»[Title/Abstract] OR «supervised learning»[Title/Abstract] OR «unsupervised learning»[Title/Abstract] OR «reinforcement learning»[Title/Abstract] OR «automated algorithms»[Title/Abstract] OR «adaptive algorithms» [Title/Abstract]) AND (implement* [Title] OR practice [Title] OR approved [Title]) AND (y_10[Filter]))

2.5. Data Extraction and Items

The data extraction variables were developed through weekly brainstorming sessions. At least four co-authors (TC, TOS, MT, PDN) participated in each brainstorming session, defining the list of topics relevant for extraction. The initial sessions were focused on the free definition of the topics and variables useful for extraction. As the brainstorming sessions advanced, the categories of variables were inductively defined, leading to the final list of agreed variables for data extraction as shown in Table 2 . The final list of extraction items was calibrated through limited tests by four co-authors.

Data extraction items.

2.6. Critical Appraisal of Individual Sources of Evidence

We used the Mixed Methods Appraisal Tool (MMAT) [ 26 ] to critically assess the quality of the included studies. MMAT was an appropriate tool because the nature of relevant studies varied widely between qualitative, quantitative and mixed methods. Three co-authors (TC, MATH, LMR) assessed the quality of the studies and disagreements were resolved by discussion.

2.7. Synthesis of Results

Qualitative methods were used to synthesize the extracted facilitators and barriers based on the Consolidated Framework for Implementation Research (CFIR) (see Figure 1 and the codebook in Additional file S5 ). CFIR is a framework used by many implementation research studies. It provides an index of constructs for organizing findings in a consistent and understandable manner [ 27 ]. It naturally invites us to follow a deductive strategy in the synthesis of results. However, due to the many technical and organizational details found in AI implementations, we considered that a more granular presentation was convenient in the synthesis of results, as previous studies in the field of clinical decision support (CDS) had shown [ 28 ]. To that end, we opted for a mixed approach, aiming to join the proven coherency of the CFIR constructs, for the general classification of barriers and facilitators, with the detailed approach that open inductive coding provided for defining items about the specific context under examination. In this way, we broke down the details of each CFIR construct in the framework’s codebook into more granular sub-constructs that were easily mappable to specific barriers and facilitators in AI implementations. With this rationale in mind, we split the analysis of results in two stages and performed a mixed inductive-deductive approach.

Theoretical constructs of the Consolidated Framework for Implementation Research (CFIR).

2.8. Open Inductive Coding and Mapping onto the CFIR Framework

Five co-authors (LMR, TOS, MATH, MT, PDN) read the full papers, extracting any section that pointed to a possible barrier or facilitator. Free comments (e.g., observations and interpretations) from the reviewers were allowed. All the papers were reviewed by at least two co-authors. Both text segments and free comments were imported into the qualitative analysis software, MaxQDA [ 29 ], for further analysis.

Two co-authors (TOS, LMR) went through the extracted segments of the selected papers independently. Initially, a deductive approach to code the segments into CFIR constructs was used. After one iteration, the constructs were considered not granular enough. Then, two reviewers (TOS, LMR) proceeded with an inductive approach, with no predefined code list. The reviewers marked all the segments of text that indicated a barrier or a facilitator for AI adoption.

Once all the papers had been coded, the reviewers met with three other members of the team, who had read all the papers but had not coded the texts. Iterative meetings were performed to go through all the coded texts and crosscheck the results. Equivalent labels were merged into one single concept when agreements were found. Any disagreements were discussed until all the members agreed on the optimal concept to code a specific fragment of the text by checking the full text and re-reading the section of interest. The usual sources of disagreement were the scope of one concept and the specific barriers and facilitators that one concept should encompass.

The same concept could be described as a barrier or as a facilitator by different studies (for instance, data quality was described as a barrier with ”insufficient data quality“ and a facilitator as ”availability of high-quality data“. This process resulted in an index of concepts that fully categorized all the barriers and facilitators found in the full texts. The index of concepts evolved iteratively until the end of this inductive analysis, refining the semantics of each concept and its scope.

The index of concepts was analyzed by the team and mapped into the constructs of the CFIR framework. Any disagreements about which CFIR construct was the most appropriate for the concept were resolved by discussing the possible options until an agreement was reached. With regards to coverage, the CFIR fully covered the concepts defined in our index, and all of the index concepts could be mapped to CFIR constructs.

3.1. Selection of Sources of Evidence

Phase one of the search resulted in a total of 607 articles, while the second phase resulted in 2177 articles, after the removal of duplicates. Four co-authors (TC, TOS, MT, PDN) independently screened the titles and abstracts according to the inclusion and exclusion criteria. This resulted in the removal of 2668 articles, leaving 116 relevant articles. A full-text assessment was conducted on these 116 relevant articles, which resulted in 19 included articles, as shown in Figure 2 , 11 of which were published in 2020.

PRISMA flow diagram based on the template by Page et al. [ 30 ]. Study characteristics and critical appraisal.

As shown in Table 3 , about half of the included studies were conducted in the USA and Canada (47%, n = 9/19); about a quarter (26%, n = 5/19) were conducted in Northeastern Asia, and only three in Europe. Several medical fields were represented: sepsis (16%, n = 3/19), diabetes (11%, n = 2/19), cardiology (11%, n = 2/19), mental health (11%, n = 2/19), emergency care (11%, n = 2/19) and palliative care (5%, n = 1/19), and the rest were for all patients (16%, n = 3/19). The most common medical task (an AI-use case) was screening (79%, n = 15/19). Only two of the studies had AI systems targeted towards use by patients, while the rest were meant to be used by clinicians or healthcare staff (89%, n = 17/19). In terms of AI algorithms, the majority of the studies applied deep learning (63%, n = 12/19).

Properties of the included studies.

In terms of the appraisal, three co-authors (TC, LMR, MATH) used the MMAT template to independently appraise the included studies (see Additional file S2 —MMAT), and any disagreements were reconciled through discussion. Except for one study, all the other studies had well-defined research questions and sufficient data to address the questions they posed. We categorized the studies into quantitative non-randomized (58%, n = 11/19), qualitative (37%, n = 7/19) and quantitative descriptive (5%, n = 1/19). In all the quantitative studies, the participants were representative of the target population. For all but one of the qualitative studies, the methods used were appropriate to answer the posed questions.

3.2. Results of Individual Sources of Evidence

The concepts identified through the inductive process were further classified into ten broader themes: evaluation and testing, background, management and engagement, data quality and management, trust and transparency, clinical workflow, interoperability, finance and resources, technical design and AI policy and regulation. As illustrated in Table 4 , most of the facilitators were based on the management and engagement theme (47%, n = 27/57). None of the reviewed articles reported barriers related to management and engagement. The second most common facilitators were related to the theme of evaluation and testing (14%, n = 8/57), while a third were related to technical design (12%, n = 7/57). For the barriers, the most common were interoperability issues (19%, n = 7/36), data quality and management (17%, n = 6/36) and trust and transparency (14%, n = 5/36).

Inductive extraction of concepts and themes.

Table 5 shows the concepts extracted from each study. Three studies had no easily discernible barriers or facilitators [ 34 , 36 , 41 ]. Three studies that reported facilitators did not report any barriers [ 38 , 39 , 47 ], and two studies that reported barriers did not report any facilitators [ 33 , 35 ].

Facilitators and barriers based on the concepts.

3.3. Mapping Extracted Concepts to CFIR

The mapping between the coded concepts and the corresponding CFIR constructs is available in [ Additional file S3 —CFIR mappings]. In total, 69 facilitators and 46 barriers were identified and coded following the CFIR framework. The result of this mapping is summarized in Figure 3 . Most of the studies reported V. Process as the main facilitator for AI implementation in healthcare (35%, n = 24/69). The second most popular facilitators were based on III. Inner setting (29%, n = 20/69), followed by I. Intervention characteristics (27%, n = 19/69). Most of the barriers were reported for I. Intervention characteristics (41%, n = 19/46), followed by III. Inner setting (33%, n = 15/46) and II. Outer setting, which had the least number of both barriers and facilitators.

Frequency of facilitators versus barriers.

4. Discussion

Viewed in total, the reporting of facilitators and barriers related to the I. Intervention characteristics, III. Inner setting and V. Process appear somewhat balanced, with some under-reporting of the II. Outer setting and the IV. Characteristics of individuals. Viewed per study, however, the reporting imbalances are more apparent, and we highlight two kinds of imbalance. The first case relates to how a study can concentrate on a single theoretic domain and neglect the rest, and the second is where, regardless of the theoretic domain, a study focuses on either one of facilitators or barriers.

In the first case of theoretic domain imbalance, some studies focused on the characteristics of the intervention [ 33 , 49 ], while others focused on the process [ 40 , 47 ]. Consequently, we end up with an incomplete picture of the implementation for any single study, and it is difficult to compare findings across studies [ 50 ]. In the second case, which was typically the case, the studies focused more on the facilitating factors than the barriers. In extreme cases, a study might focus on facilitators alone [ 38 , 39 , 47 ] and completely neglect the barriers, or the other way round [ 33 , 35 ]. We attribute this poor reporting and imbalance to a lack of implementation science expertise.

We further describe some of the salient highlights in each of the five CFIR domains. These highlights are based on the frequency of discussion they generated in the included studies. The major facilitating factors were related to the implementation process itself and the involvement of users, and this finding is consistent with the literature [ 51 ]. In contrast, barriers were mostly associated with the intervention characteristics and inner setting, specifically interoperability, trust and transparency and non-availability of high-quality data.

4.1. Intervention Characteristics

4.1.1. evidence strength and quality.

ML algorithms need to continuously learn from new data. As data change and as methodological techniques advance, so must the models, and this presents several challenges. One of challenges is the continuous need to validate algorithms and test whether their specificity and sensitivity have deteriorated. This partially explains why many of the included studies conducted fresh validation tests.

In the validation process, it is essential to make sure the training data represents the population to which the AI system is applied. In practice, results may not be representative across populations [ 43 , 48 ], and experiences may not be generalizable to a new setting [ 40 ]. Projects that have been properly evaluated and tested are more likely to succeed in the implementation process. In this regard, [ 49 ] recognized clinical trials and multi-center studies are a necessary part of implementation [ 33 , 49 ].

4.1.2. Design Quality and Complexity

Technical design decisions might affect the implementation by facilitating or hindering this process. Usability was cited as both an important facilitator and a barrier. For instance, Romero-Brufau et al. [ 43 ] faced problems related to the documentation and presentation of results, and reported difficulties understanding patient information from the decision support system. They dedicated two months to refining the interface of the system and adapting it to the workflow, reflecting the importance of customization in the implementation process. Intuitive, unintrusive, and easy-to-use systems have better chances to succeed in the implementation process [ 42 , 45 ].

4.1.3. Interoperability, Adaptability and Generalizability

In order to successfully implement an AI system in a clinical workflow, the system must interoperate with the targeted hospital systems. In [ 46 ], the lack of data interoperability was exposed when overworked nurses were asked to print and deliver medical histories in paper form. This resulted in a situation where medical histories were often not printed, and thus were not provided to physicians. Data interoperability issues led to poor integration in the clinical workflow.

4.1.4. Integration with Clinical Workflow

A lack of integration with the clinical workflow can be a barrier to the implementation process. For example, Sendak et al. [ 40 ] reported workflow issues with model retraining and updating, which are intrinsic ML processes. In addition, projects that are too technically complex or disruptive are at risk of hindering the implementation process:

“Models that require additional work, even if it is as little as looking at another screen and clicking a few more times, are much less likely to be implemented or sustained” [ 44 ].

As a solution, some studies showed that ML-based methods compatible with logic-based CDS methods are easier to integrate in the clinical workflow. An example is the use of neural networks for knowledge discovery during the development stage, where results have been later discretized as Arden syntax ECA rules in the production stage [ 35 ].

4.2. Outer Setting

External policies and incentives.

The outer setting was discussed by only one study [ 35 ], mostly from the perspective of the legislative environment as a barrier, and the study was conducted in Europe, where AI algorithms used in healthcare are considered Software as a Medical Device (SaMD) and require CE-certification by law. This certification is expensive and time-consuming. However, an exemption allows AI software under clinical evaluation to be used without CE conformity. This requires only an approval from an ethical board and a study protocol adhered to for auditing. This exemption is generally utilized due to costs related to certification [ 35 ].

4.3. Inner Setting

Resource availability.

The availability of high-quality data resources within the organization was discussed as an important determinant factor. Most of the studies used electronic health records (EHR) as the primary source of data, and they reported their complexity and inadequate use as a barrier [ 37 , 42 ]. Due to the complex nature of the EHR, key data that can be used to predict the outcome of interest is not always available or ready in the structured format for AI algorithms [ 31 , 37 , 43 ].

“…key data that reliably predict the outcome of interest may not be readily available as structured, discrete data inputs from the EHR…” [ 43 ]

Missing data, noisy data, or data without proper labels and identifiers were among the main factors that lowered data quality and were consequently reported as barriers. Besides data quality, the frequency of data updates is another important issue in maintaining the validity of predictive models [ 33 ]. Although data quality and management were usually seen as a barrier by most of the studies, Lee et al. [ 31 ] mentioned that rich data availability was a facilitator of the implementation process.

4.4. Characteristics of Individuals

Knowledge, beliefs and other personal attributes.

This domain speaks to the perceptions and beliefs of the individuals involved in the implementation. For instance, a lack of trust among clinicians might hinder the implementation process. The clinicians must trust that the system maintains good sensitivity and specificity and provides trustworthy suggestions in line with evidence-based practice and clinical judgement. As Sendak et al. [ 40 ] note:

“Clinical leaders prioritized positive predictive value as a performance measure and were willing to trade-off model interpretability for performance gains”.

At the beginning of the implementation process in [ 46 ], the physicians showed interest in the use of an AI-based decision support system that improves diagnostics. However, two of them reported errors in the medical histories, which led them to a wrong diagnosis. As a consequence of sharing those reports among the physicians, the decision support system was perceived as prone to error, generating persistent distrust, and so undermining the usefulness of the system.

Another factor is explainability, a characteristic that directly conditions the transparency and trust of the AI implementation, which, in turn, are precursors of privacy and fairness [ 52 ]. No explainability technique is a one-size-fits-all solution for every intervention. Each AI system needs to adapt its explainability to the context and the audience using the model. For instance, a CDS based on a logistic regression model is perfectly understandable by clinicians, but it may be opaque in the context of a patient-oriented app. Other models, such as neural networks, are generally opaque and could be complemented with recent discoveries in explainability techniques such as feature relevance or visualization [ 53 , 54 , 55 ].

4.5. Process

Champions and key stakeholders.

User involvement ranked as the most reported facilitator, followed by the education of key stakeholders. In the very beginning of a project, it is useful to have a common justification [ 47 ] and an early mapping of the workflow [ 43 ]. In order to attain this, it is necessary to get the relevant participants on board as early as possible [ 43 , 44 , 48 ]. The stakeholders’ feedback and involvement, especially from the leadership, clinicians and users, are also necessary throughout the implementation process [ 32 , 38 , 46 , 48 ]. In many instances, the projects strongly supported by the leadership have a higher probability to succeed. Senior leadership support can be crucial to achieve a shared vision among different stakeholders to reach the desired impact.

4.6. Implication of the Results and Recommendations for the Future

The barriers and facilitating factors emerging from this study are not surprising, since they are widely reported in the literature. The included studies presumably have overcome many of the barriers since the studies are based on the late stages of implementation. We expected that insight into the determinants of their successes would shed new light on our basic understanding of AI implementation in clinical settings. However, what we uncovered was insufficient and imbalanced reporting of some key theoretical domains, which suggests a lack of implementation science expertise in the reporting of relevant projects.

The traditional recommendation for e-health implementation processes is to involve both ICT and clinical domain experts. All the included studies seem to have followed this basic recommendation, but our findings suggest there still is a missing piece of the puzzle-socio-organizational considerations. Considering the successes of implementation science as a field, perhaps it is time we looked beyond these traditional recommendations in order to uncover additional synergies based on new modes of inquiry native to implementation science, integrating insights from social science theories and abstractions.

We also showed that domain differences between AI and implementation science have an impact on multidisciplinary research. Since implementation has become a key aspect of AI in healthcare, it is important to unify the vocabulary to make relevant research more accessible to both fields. This could start with annotating relevant publications with an appropriate keyword indicating the implementation stage or purpose of the study, for example, using Curran et al.’s [ 19 ] Hybrid Types or research pipeline model (ibid.). Classifying implementation stages is an important problem [ 56 ] and may reduce the ambiguity of terminology and bridge the gap between data science and implementation science.

4.7. Limitations

Perhaps one of the major limitations of this study is the uncertainty regarding coverage of the relevant literature, which was conditioned by multiple factors. First, we noted that some AI implementations might not have been subject to rigorous scientific study or evaluation, while other implementations were only reported locally in internal reports. This made the implementations essentially inaccessible. In addition, ambiguity related to terminology was a huge factor in successfully identifying all the relevant studies. We allude to the difficulties of defining implementation and the consequences it had on our search strategy and screening.

It is possible that attentional bias is a factor in our findings. Since we set out to identify advanced implementations, it is conceivable these implementations faced comparatively fewer challenging barriers than those of a typical implementation. This might partially explain why there were many more facilitating factors than barriers. In looking at successful implementations, it is quite possible we missed many important barriers from failed implementations.

5. Conclusions

This study exemplifies a theory-based approach to synthesizing determinants of AI implementation success and formalizes known gaps and biases related to how AI implementations are reported. In addition to highlighting the major facilitators and barriers, we noted a widespread imbalance and insufficient reporting of AI implementations in clinical settings. We single out the II. Outer setting and IV. Characteristics of individuals as two key theoretical domains, which were not fully explored in the included studies. As a result, we know very little about the knowledge and beliefs, self-efficacy and other personal attributes of the people involved in the implementations. Similarly, any policies, incentives, collaborative networks or competitive pressures that helped or hindered these implementations are largely unknown. These factors represent an important knowledge gap and require further inquiry before AI implementation in healthcare can be more fully understood.

Further, we recommend two remedial actions based on our findings: (i) implementation science expertise should be a part of every AI implementation project in healthcare in order to improve both the implementation process and the quality of scientific reporting, and (ii) scientific publications involving AI implementations in clinical settings should be annotated with an implementation stage or purpose to make relevant research more easily accessible.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph192316359/s1 , Additional File S1—Search strings; Additional File S2—MMAT critical appraisal; Additional File S3—CFIR mappings; Additional File S4—PRISMA checklist; Additional File S5—CFIR codebook.

Funding Statement

This study was funded by the Norwegian Centre for E-health Research.

Author Contributions

P.D.N. conceived the idea as project manager; T.C. designed the approach for the study; M.T. (Miguel Tejedor), T.O.S., T.C., P.D.N. screened, T.O.S., L.M.-R., M.T. (Maryam Tayefi), M.T. (Miguel Tejedor) extracted, T.O.S., L.M.-R. analyzed and M.T. (Maryam Tayefi), P.D.N. interpreted the data; K.L. made a substantial contribution to the search strategy; A.M. (Anne Moen), F.G., A.M. (Alexandra Makhlysheva), L.I., K.L. reviewed the manuscript and substantively revised it. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The potential for artificial intelligence to transform healthcare: perspectives from international health leaders

Published date

Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI’s potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.

Duke-Margolis Authors

Christina Silcox, PhD

Research Director, Digital Health Adjunct Assistant Professor Senior Team Member Margolis Core Faculty

Katie Huber, MPH

Senior Policy Analyst

Robert Saunders, PhD

Senior Research Director, Health Care Transformation Adjunct Associate Professor Executive Team Member Margolis Core Faculty

Mark McClellan, MD, PhD

Director of the Duke-Margolis Institute for Health Policy Robert J. Margolis, MD, Professor of Business, Medicine and Policy Margolis Executive Core Faculty

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Artificial Intelligence in Healthcare

Jul 17, 2014

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Artificial Intelligence in Healthcare. Casey C. Bennett. 1 Dept. of Informatics Centerstone Research Institute Nashville, TN, USA 2 School of Informatics and Computing Indiana University Bloomington, IN, USA. Cognitive Offload.

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Artificial Intelligence in Healthcare Casey C. Bennett • 1Dept. of Informatics • Centerstone Research Institute • Nashville, TN, USA • 2School of Informatics and Computing • Indiana University • Bloomington, IN, USA

Cognitive Offload

We can make predictions and discover patterns, but what do you do with them?

Building a Pipeline Data Patterns Predictions Temporal Models Decisions

Patterns Step 3 Step 2 Step 1 Predictions Make Predictions Make Decisions Transition Models Clinical Indicators Socio-demographic a = Action/Treatment s = Patient State o = Observation c = Costs t = Time cpuc = Utility Patterns Genetic Data Etc. Markov Decision Processes (MDPs) Machine Learning/Statistical Techniques – predict risk stratification, treatment response , survival, re-hospitalization, LOS, etc. Dynamic Decision Networks (DDNs)

Step 3 Make Decisions a = Action/Treatment s = Patient State o = Observation c = Costs t = Time cpuc = Utility Belief States Plan over Time Plan/re-plan

Bennett CC and K Hauser (2013) “Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach.” Artificial Intelligence in Medicine. 57(1): 9-19.

Simplification

1 2 Components to classify/cluster/etc. Components that determine optimal actions, based on those patterns Find Patterns Make Decisions

Relativity Natural Selection Einstein Darwin

Cognitive Computing IBM and others

Botvinick M (2012) “Heirarchical Relational Learning and Decision Making.” Current Opinions in Neurobiology. 22(6): 956-962.

The Future? Personalized AI?

Ongoing Collaborations

Thank you • www.CaseyBennett.com • http://r-house.soic.indiana.edu/ - IU Human-Robot Interaction Lab • www.CenterstoneResearch.org - CRI

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Artificial Intelligence in Asia | Artificial Intelligence Courses in Kolkata

Getting started with AI? Enroll in our Artificial Intelligence Training course in Kolkata which gives an overview of principles & approaches of AI.

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Top 4 Applications of Artificial Intelligence in Healthcare

PPT enlightening the Top 4 Applications of Artificial Intelligence in Healthcare and use of AI-based machines and medical equipment detecting diseases and analyzing the reports for making the quick decision and provide better and faster medical treatments to patients. Cogito explains what the top four use of Artificial Intelligence are in Healthcare industry and how this technology is helping doctors, attendants and other medical experts to make the medical care and diagnosis process more effective and useful. Read Blog: https://goo.gl/LTh89c

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Artificial Intelligence Service in Healthcare

It is no secret that artificial intelligence is shaping new business landscapes in every industries. As one of emerging convergence technologies, Artificial Intelligence (AI) creates new products and services, finally innovating business models. Especially, it has been noted by industry experts and researchers that healthcare sector has the biggest potential of AI convergence.

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Top Investment Pockets in Artificial Intelligence in Healthcare Market

The global AI in healthcare market was valued at $1,441 million in 2016, and is estimated to reach at $22,790 million by 2023, registering a CAGR of 48.7% from 2017 to 2023.

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Artificial Intelligence Use in the Healthcare Industry

AI is used in healthcare in diverse treatment methods as well as to provide important solutions such as medical transcription service.

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Artificial Intelligence In The Real World Of Healthcare

It’s here! Artificial Intelligence (AI) is on the scene and on the rise in the real world of healthcare. Long gone are the days when a medical practitioner was up on all the latest diseases, medications and treatments/procedures in healthcare due to the vast explosion of information and technologies in today’s ever-changing world. Providers have begun to rely on technology to augment their knowledge and assist in treatments and procedures. The reliance on computerization has hit home and many advantages have been realized and continue to expand. Then, along came AI! AI can literally be incorporated into every area of patient care. AI’s amazing and wonderful impact so far on modern healthcare will be shared.

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Impact of Artificial Intelligence on Healthcare

Some of the visible areas where AI has helped are in treating patients, providing medical facilities, designing medical instruments, the way surgeries are performed, in the detailed diagnosis of the ailments and making the treatment reach even in the remotest areas with Virtual Consultation.

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Global Healthcare Artificial Intelligence Market

Global Healthcare Artificial Intelligence Market is likely to grow at CAGR of around 41% and will exceed over USD 5.0 billion by 2024.

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Artificial Intelligence (AI) Is Transforming Healthcare Industry

Healthcare AI is a brand new approach. More like Machine Learning (Artificial Intelligence into Healthcare Industry). Now, itu2019s a way to give Reliable, Accurate & Concise Medical Care to Patients. In fact, Big Hospitals around the world have started investing in Healthcare AI Applications.

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Medical Billing and Coding revenue cycle is very important and growing segment. Procedures for billing and coding are necessary and theyu2019ve used to translate patient records into standard codes. These codes are used for billing third party payers and patients. Correct coding is a challenge. Total $36 billion resulted in improper payments in year 2017 (according to Centers for Medicare & Medicaid Services)

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Global Artificial Intelligence HealthCare Market 2020-2027

The global Healthcare Artificial Intelligence market expected to grow with a substantial rate in the forecast period, 2020-2027. Artificial intelligence has quickly become the main topic of discussion among healthcare professionals, vendors, and IT developers. Browse our full report with Table of Content : https://www.bharatbook.com/marketreports/global-artificial-intelligence-healthcare-market-by-offering-hardware-software-and-services-by-technology-machine-l/1973609

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Artificial Intelligence in Healthcare Market Highlights

Artificial Intelligence in Healthcare Market research report presents a comprehensive assessment of the Artificial Intelligence in Healthcare Market and contains thoughtful insights, facts, historical data, and statistically supported and industry-validated market data. Artificial Intelligence in Healthcare Market Overview and the Impact of COVID-19 on the global Artificial Intelligence in Healthcare Market. Artificial Intelligence in Healthcare Market Research Report is an inestimable supply of perceptive information for business strategists.

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Artificial Intelligence Chatbots In Healthcare Industry

AI and Chatbots are uniquely positioned to address the needs of the healthcare industry that is challenged in delivering the right message, the right response at the right time, and also in capturing the right voice of the consumer. The ability to bring everyone together in a conversational way will pave the way to not only in delivering the right care but also in formulating the right responses that address the needs of targeted consumers at a faster pace.

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10 Common Applications of Artificial Intelligence in Healthcare

List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/

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Healthcare artificial intelligence market

Artificial intelligence (AI) or machine intelligence technology using complex algorithms which enables machines to sense, comprehend, and learn tasks requiring general or human intelligence.

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Advancement of Artificial Intelligence in Healthcare Industry

Scientific progress is all about occasionally big and a number of small advancements. Artificial Intelligence (AI) has created new technology which can handle huge data sets and solve complex problems which was previously done by human intelligence. Artificial Intelligence in Healthcare Industry has helped to integrate data and develop new insights on public as well as individual health.

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Home Collections Technology Artificial Intelligence Artificial Intelligence In Healthcare PPT

AI in Healthcare PowerPoint Template and Google Slides

AI in Healthcare Presentation Slide

This cutting-edge AI in healthcare PowerPoint template empowers you to deliver impactful presentations on the transformative role of Artificial Intelligence in medicine. Packed with visually engaging and editable slides, it's perfect for science and research presentations , investor pitches, or internal training sessions. Highlight key applications like early disease detection, personalized medicine, and optimized hospital operations. Showcase the latest research and trends with stunning data visualizations and infographics. Whether you're a healthcare professional, researcher, or entrepreneur, this template equips you to communicate the power of AI in shaping the future of healthcare.

Features of the template:

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Shifting the healthcare ai discourse: embracing intelligence assistance.

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CEO of Bamboo Health . Bringing innovation and growth to the forefront of healthcare with past leadership roles at Lumeris and CVS Health.

In the ever-evolving healthcare landscape, the integration of artificial intelligence (AI) continues to spark enthusiasm with its potential to revolutionize patient care and streamline processes. From analyzing radiological images to mining electronic health records for diagnoses, AI promises a transformative shift toward enhanced efficiency and improved outcomes.

However, amid the buzz surrounding AI’s capabilities, it is crucial to consider its implications on human decision-making, especially within healthcare where outcomes are crucial to a patient’s overall physical and mental well-being.

Will AI replace clinicians and disrupt the traditional healthcare model as many fear ? Will the technology produce unreliable information that can lead to reputational, compliance and operational risks for healthcare organizations, not to mention patient harm? Or will AI instead empower healthcare professionals to prioritize empathetic patient care while leveraging AI-supported insights?

From AI To IA

In an organization where we navigate hundreds of millions of pivotal moments in an individual’s care journey annually, I advocate for a paradigm shift toward intelligence assistance (IA) as opposed to autonomous AI. This concept emphasizes augmenting human decision-making rather than replacing it, promising not only to reshape healthcare but various industries across the board.

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Further, IA promotes taking action. While AI today presents a variety of options, it often stops short of helping facilitate the next step. On the other hand, IA combines the knowledge of artificial intelligence with human-informed decision-making, guiding users to the best possible plan and empowering them to execute the plan effectively.

The necessity for IA in healthcare is evident, as the broad use of AI requires organizations to build “information skepticism” into the culture to mitigate overreliance on the sometimes untrustworthy results surfaced by AI. Moreover, the medical field has and always will require a human touch and a provider’s learned expertise. This is particularly important in scenarios such as managing mental health or substance use disorders in emergency departments (ED). Often, ED clinicians lack the necessary niche expertise and resources to provide specialized support in these cases.

Here, IA serves as a formidable ally, aiding care teams in assessing patients comprehensively and considering various crucial factors—such as insurance coverage, clinical expertise, transportation accessibility and cultural sensitivity—when recommending the next course of action. In turn, these care teams can carry out the plan more confidently and efficiently. This collaborative approach has the potential to revolutionize the healthcare system, particularly in providing whole-person care tailored to individual needs.

The Difference Between AI And IA

What distinguishes IA is its fundamental approach, prioritizing the synergy between human understanding and machine intelligence. This collaboration offers several advantages:

• Decision Support: IA serves as an advanced advisory tool, ensuring decisions are informed by the latest data, thus enhancing their accuracy and relevance.

• Collaboration Versus Replacement: IA aims not to replace human roles but to foster a synergistic partnership that leads to unprecedented outcomes.

• Enhancing Expertise: IA amplifies human capabilities across various domains, enriching rather than diminishing expertise.

• Prompt Problem Solving: In our fast-paced world, IA facilitates swift problem-solving by merging rapid data analysis with human insight.

• Informed Action: IA encourages users to take immediate and effective action as they’re equipped with the proper knowledge to move forward more confidently.

Ensuring A Successful Transition

The journey toward harnessing the full potential of IA begins with acknowledging the pitfalls and challenges of AI itself. Crucially, the technology is far from infallible. A 2024 study found that the most advanced large language models were able to identify and correct their own mistakes only 53% of the time.

With that in mind, the process of implementing IA should begin with identifying which roles and responsibilities might be augmented by AI and which might potentially be replaced by AI. Some nonclinical healthcare roles that can benefit from the use of IA include software engineers (whose work can be accelerated using AI tools) or the authors of internal documents (who can use generative AI to create first drafts for careful human review).

Intelligence assistance can also make use of AI to provide data literacy at the point of care—natural language summaries of critical data that are fine-tuned for the end user’s specific credential level to be meaningful for the work they do. For instance, deploying IA to assist in identifying patients with complex conditions such as substance use disorders or chronic diseases can significantly help improve care coordination and outcomes.

Bear in mind, though, that most roles within healthcare will never be fully replaced by AI. One cautionary example is the National Eating Disorder Association’s decision last year to shut down a chatbot intended to replace volunteers who operated a hotline for people with eating disorders. The chatbot had been giving callers information that was “harmful and unrelated to the program,” NEDA said . Using an IA approach in such an instance, hotline volunteers could have moderated the AI-produced insights while providing a much-needed human touch for the callers.

As healthcare organizations navigate the complexities of integrating IA, it’s important to recognize that this is not a one-size-fits-all solution. Tailoring IA strategies to address specific organizational challenges and patient needs is key to realizing its benefits. Fostering a culture of information skepticism that can safely build the use of AI into the care process while maintaining the all-important human touch is crucial. By doing so, healthcare organizations can create a more humane, efficient and effective healthcare delivery system that leverages the best of both human and artificial intelligence.

Final Thoughts

The shift from AI to IA signifies an acknowledgment that optimal solutions emerge from the fusion of human and machine intelligence. In healthcare and beyond, this transition promises faster, more accurate and more empathetic responses to human needs. Ultimately, intelligence assistance represents not just technological progress but a stride toward a more collaborative, informed and efficient future that never loses the human touch in healthcare and beyond.

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Related Practices & Jurisdictions

• Health Law & Managed Care
• Biotech, Food, Drug
• Communications, Media & Internet
• Administrative & Regulatory
• All Federal

Benjamin Zegarelli and Pat Ouellete discussed the health care regulations governing medical devices and software and shared how to stay informed and navigate the evolving landscape of health care regulations. This session explored:

• FDA’s current approach to regulating AI/ML devices, including recent marketing authorization decisions and enforcement actions
• Other health care applications of AI/ML and the accompanying regulatory landscape
• Potential pitfalls and emerging regulations
• New regulations for EMR developers
• Proposed non-discrimination requirements in connection with health care provider use of AI by the Office for Civil Rights
• The recent White House AI Executive Order and the sections relevant to use of AI in health care
• Recent proposed legislation at the state and federal levels

Click  here  to see the slides from this presentation.

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