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40 Detailed Artificial Intelligence Case Studies [2024]

In this dynamic era of technological advancements, Artificial Intelligence (AI) emerges as a pivotal force, reshaping the way industries operate and charting new courses for business innovation. This article presents an in-depth exploration of 40 diverse and compelling AI case studies from across the globe. Each case study offers a deep dive into the challenges faced by companies, the AI-driven solutions implemented, their substantial impacts, and the valuable lessons learned. From healthcare and finance to transportation and retail, these stories highlight AI’s transformative power in solving complex problems, optimizing processes, and driving growth, offering insightful glimpses into the potential and versatility of AI in shaping our world.

Related: How to Become an AI Thought Leader?

1. IBM Watson Health: Revolutionizing Patient Care with AI

Task/Conflict: The healthcare industry faces challenges in handling vast amounts of patient data, accurately diagnosing diseases, and creating effective treatment plans. IBM Watson Health aimed to address these issues by harnessing AI to process and analyze complex medical information, thus improving the accuracy and efficiency of patient care.

Solution: Utilizing the cognitive computing capabilities of IBM Watson, this solution involves analyzing large volumes of medical records, research papers, and clinical trial data. The system uses natural language processing to understand and process medical jargon, making sense of unstructured data to aid medical professionals in diagnosing and treating patients.

Overall Impact:

Enhanced accuracy in patient diagnosis and treatment recommendations.
Significant improvement in personalized healthcare services.

Key Learnings:

AI can complement medical professionals’ expertise, leading to better healthcare outcomes.
The integration of AI in healthcare can lead to significant advancements in personalized medicine.

2. Google DeepMind’s AlphaFold: Unraveling the Mysteries of Protein Folding

Task/Conflict: The scientific community has long grappled with the protein folding problem – understanding how a protein’s amino acid sequence determines its 3D structure. Solving this problem is crucial for drug discovery and understanding diseases at a molecular level, yet it remained a formidable challenge due to the complexity of biological structures.

Solution: AlphaFold, developed by Google DeepMind, is an AI model trained on vast datasets of known protein structures. It assesses the distances and angles between amino acids to predict how a protein folds, outperforming existing methods in terms of speed and accuracy. This breakthrough represents a major advancement in computational biology.

Significant acceleration in drug discovery and disease understanding.
Set a new benchmark for computational methods in biology.
AI’s predictive power can solve complex biological problems.
The application of AI in scientific research can lead to groundbreaking discoveries.

3. Amazon: Transforming Supply Chain Management through AI

Task/Conflict: Managing a global supply chain involves complex challenges like predicting product demand, optimizing inventory levels, and streamlining logistics. Amazon faced the task of efficiently managing its massive inventory while minimizing costs and meeting customer demands promptly.

Solution: Amazon employs sophisticated AI algorithms for predictive inventory management, which forecast product demand based on various factors like buying trends, seasonality, and market changes. This system allows for real-time adjustments, adapting swiftly to changing market dynamics.

Reduced operational costs through efficient inventory management.
Improved customer satisfaction with timely deliveries and availability.
AI can significantly enhance supply chain efficiency and responsiveness.
Predictive analytics in inventory management leads to reduced waste and cost savings.

4. Tesla’s Autonomous Vehicles: Driving the Future of Transportation

Task/Conflict: The development of autonomous vehicles represents a major technological and safety challenge. Tesla aimed to create self-driving cars that are not only reliable and safe but also capable of navigating complex traffic conditions without human intervention.

Solution: Tesla’s solution involves advanced AI and machine learning algorithms that process data from various sensors and cameras to understand and navigate the driving environment. Continuous learning from real-world driving data allows the system to improve over time, making autonomous driving safer and more efficient.

Leadership in the autonomous vehicle sector, enhancing road safety.
Continuous improvements in self-driving technology through AI-driven data analysis.
Continuous data analysis is key to advancing autonomous driving technologies.
AI can significantly improve road safety and driving efficiency.

Related: High-Paying AI Career Options

5. Zara: Fashioning the Future with AI in Retail

Task/Conflict: In the fast-paced fashion industry, predicting trends and managing inventory efficiently are critical for success. Zara faced the challenge of quickly adapting to changing fashion trends while avoiding overstock and meeting consumer demand.

Solution: Zara employs AI algorithms to analyze fashion trends, customer preferences, and sales data. The AI system also assists in managing inventory, ensuring that popular items are restocked promptly and that stores are not overburdened with unsold products. This approach optimizes both production and distribution.

Increased sales and profitability through optimized inventory.
Enhanced customer satisfaction by aligning products with current trends.
AI can accurately predict consumer behavior and trends.
Effective inventory management through AI can significantly impact business success.

6. Netflix: Personalizing Entertainment with AI

Task/Conflict: In the competitive streaming industry, providing a personalized user experience is key to retaining subscribers. Netflix needed to recommend relevant content to each user from its vast library, ensuring that users remained engaged and satisfied.

Solution: Netflix developed an advanced AI-driven recommendation engine that analyzes individual viewing habits, ratings, and preferences. This personalized approach keeps users engaged, as they are more likely to find content that interests them, enhancing their overall viewing experience.

Increased viewer engagement and longer watch times.
Higher subscription retention rates due to personalized content.
Personalized recommendations significantly enhance user experience.
AI-driven content curation is essential for success in digital entertainment.

7. Airbus: Elevating Aircraft Maintenance with AI

Task/Conflict: Aircraft maintenance is crucial for ensuring flight safety and operational efficiency. Airbus faced the challenge of predicting maintenance needs to prevent equipment failures and reduce downtime, which is critical in the aviation industry.

Solution: Airbus implemented AI algorithms for predictive maintenance, analyzing data from aircraft sensors to identify potential issues before they lead to failures. This system assesses the condition of various components, predicting when maintenance is needed. The solution not only enhances safety but also optimizes maintenance schedules, reducing unnecessary inspections and downtime.

Decreased maintenance costs and reduced aircraft downtime.
Improved safety with proactive maintenance measures.
AI can predict and prevent potential equipment failures.
Predictive maintenance is essential for operational efficiency and safety in aviation.

8. American Express: Securing Transactions with AI

Task/Conflict: Credit card fraud is a significant issue in the financial sector, leading to substantial losses and undermining customer trust. American Express needed an efficient way to detect and prevent fraudulent transactions in real-time.

Solution: American Express utilizes machine learning models to analyze transaction data. These models identify unusual patterns and behaviors indicative of fraud. By constant learning from refined data, the system becomes increasingly accurate in detecting fraudulent activities, providing real-time alerts and preventing unauthorized transactions.

Minimized financial losses due to reduced fraudulent activities.
Enhanced customer trust and security in financial transactions.
Machine learning is highly effective in fraud detection.
Real-time data analysis is crucial for preventing financial fraud.

Related: Is AI a Good Career Option for Women?

9. Stitch Fix: Tailoring the Future of Fashion Retail

Task/Conflict: In the competitive fashion retail industry, providing a personalized shopping experience is key to customer satisfaction and business growth. Stitch Fix aimed to offer customized clothing selections to each customer, based on their unique preferences and style.

Solution: Stitch Fix uses AI and algorithms analyze customer feedback, style preferences, and purchase history to recommend clothing items. This personalized approach is complemented by human stylists, ensuring that each customer receives a tailored selection that aligns with their individual style.

Increased customer satisfaction through personalized styling services.
Business growth driven by a unique, AI-enhanced shopping experience.
AI combined with human judgment can create highly effective personalization.
Tailoring customer experiences using AI leads to increased loyalty and business success.

10. Baidu: Breaking Language Barriers with Voice Recognition

Task/Conflict: Voice recognition technology faces the challenge of accurately understanding and processing speech in various languages and accents. Baidu aimed to enhance its voice recognition capabilities to provide more accurate and user-friendly interactions in multiple languages.

Solution: Baidu employs deep learning algorithms for voice and speech recognition, training its system on a diverse range of languages and dialects. This approach allows for more accurate recognition of speech patterns, enabling the technology to understand and respond to voice commands more effectively. The system continuously improves as it processes more voice data, making technology more accessible to users worldwide.

Enhanced user interaction with technology in multiple languages.
Reduced language barriers in voice-activated services and devices.
AI can effectively bridge language gaps in technology.
Continuous learning from diverse data sets is key to improving voice recognition.

11. JP Morgan: Revolutionizing Legal Document Analysis with AI

Task/Conflict: Analyzing legal documents, such as contracts, is a time-consuming and error-prone process. JP Morgan sought to streamline this process, reducing the time and effort required while increasing accuracy.

Solution: JP Morgan implemented an AI-powered tool, COIN (Contract Intelligence), to analyze legal documents quickly and accurately. COIN uses NLP to interpret and extract relevant information from contracts, significantly reducing the time required for document review.

Dramatic reduction in time required for legal document analysis.
Increased accuracy and reduced human error in contract interpretation.
AI can efficiently handle large volumes of data, offering speed and accuracy.
Automation in legal processes can significantly enhance operational efficiency.

12. Microsoft: AI for Accessibility

Task/Conflict: People with disabilities often face challenges in accessing technology. Microsoft aimed to create AI-driven tools to enhance accessibility, especially for individuals with visual, hearing, or cognitive impairments.

Solution: Microsoft developed a range of AI-powered tools including applications for voice recognition, visual assistance, and cognitive support, making technology more accessible and user-friendly. For instance, Seeing AI, an app developed by Microsoft, helps visually impaired users to understand their surroundings by describing people, texts, and objects.

Improved accessibility and independence for people with disabilities.
Creation of more inclusive technology solutions.
AI can significantly contribute to making technology accessible for all.
Developing inclusive technology is essential for societal progress.

Related: How to get an Internship in AI?

13. Alibaba’s City Brain: Revolutionizing Urban Traffic Management

Task/Conflict: Urban traffic congestion is a major challenge in many cities, leading to inefficiencies and environmental concerns. Alibaba’s City Brain project aimed to address this issue by using AI to optimize traffic flow and improve public transportation in urban areas.

Solution: City Brain uses AI to analyze real-time data from traffic cameras, sensors, and GPS systems. It processes this information to predict traffic patterns and optimize traffic light timing, reducing congestion. The system also provides data-driven insights for urban planning and emergency response coordination, enhancing overall city management.

Significant reduction in traffic congestion and improved urban transportation.
Enhanced efficiency in city management and emergency response.
AI can effectively manage complex urban systems.
Data-driven solutions are key to improving urban living conditions.

14. Deep 6 AI: Accelerating Clinical Trials with Artificial Intelligence

Task/Conflict: Recruiting suitable patients for clinical trials is often a slow and cumbersome process, hindering medical research. Deep 6 AI sought to accelerate this process by quickly identifying eligible participants from a vast pool of patient data.

Solution: Deep 6 AI employs AI to sift through extensive medical records, identifying potential trial participants based on specific criteria. The system analyzes structured and unstructured data, including doctor’s notes and diagnostic reports, to find matches for clinical trials. This approach significantly speeds up the recruitment process, enabling faster trial completions and advancements in medical research.

Quicker recruitment for clinical trials, leading to faster research progress.
Enhanced efficiency in medical research and development.
AI can streamline the patient selection process for clinical trials.
Efficient recruitment is crucial for the advancement of medical research.

15. NVIDIA: Revolutionizing Gaming Graphics with AI

Task/Conflict: Enhancing the realism and performance of gaming graphics is a continuous challenge in the gaming industry. NVIDIA aimed to revolutionize gaming visuals by leveraging AI to create more realistic and immersive gaming experiences.

Solution: NVIDIA’s AI-driven graphic processing technologies, such as ray tracing and deep learning super sampling (DLSS), provide highly realistic and detailed graphics. These technologies use AI to render images more efficiently, improving game performance without compromising on visual quality. This innovation sets new standards in gaming graphics, making games more lifelike and engaging.

Elevated gaming experiences with state-of-the-art graphics.
Set new industry standards for graphic realism and performance.
AI can significantly enhance creative industries, like gaming.
Balancing performance and visual quality is key to gaming innovation.

16. Palantir: Mastering Data Integration and Analysis with AI

Task/Conflict: Integrating and analyzing large-scale, diverse datasets is a complex task, essential for informed decision-making in various sectors. Palantir Technologies faced the challenge of making sense of vast amounts of data to provide actionable insights for businesses and governments.

Solution: Palantir developed AI-powered platforms that integrate data from multiple sources, providing a comprehensive view of complex systems. These platforms use machine learning to analyze data, uncover patterns, and predict outcomes, assisting in strategic decision-making. This solution enables users to make informed decisions in real-time, based on a holistic understanding of their data.

Enhanced decision-making capabilities in complex environments.
Greater insights and efficiency in data analysis across sectors.
Effective data integration is crucial for comprehensive analysis.
AI-driven insights are essential for strategic decision-making.

Related: Surprising AI Facts & Statistics

17. Blue River Technology: Sowing the Seeds of AI in Agriculture

Task/Conflict: The agriculture industry faces challenges in increasing efficiency and sustainability while minimizing environmental impact. Blue River Technology aimed to enhance agricultural practices by using AI to make farming more precise and efficient.

Solution: Blue River Technology developed AI-driven agricultural robots that perform tasks like precise planting and weed control. These robots use ML to identify plants and make real-time decisions, such as applying herbicides only to weeds. This targeted approach reduces chemical usage and promotes sustainable farming practices, leading to better crop yields and environmental conservation.

Significant reduction in chemical usage in farming.
Increased crop yields through precision agriculture.
AI can contribute significantly to sustainable agricultural practices.
Precision farming is key to balancing productivity and environmental conservation.

18. Salesforce: Enhancing Customer Relationship Management with AI

Task/Conflict: In the realm of customer relationship management (CRM), personalizing interactions and gaining insights into customer behavior are crucial for business success. Salesforce aimed to enhance CRM capabilities by integrating AI to provide personalized customer experiences and actionable insights.

Solution: Salesforce incorporates AI-powered tools into its CRM platform, enabling businesses to personalize customer interactions, automate responses, and predict customer needs. These tools analyze customer data, providing insights that help businesses tailor their strategies and communications. The AI integration not only improves customer engagement but also streamlines sales and marketing efforts.

Improved customer engagement and satisfaction.
Increased business growth through tailored marketing and sales strategies.
AI-driven personalization is key to successful customer relationship management.
Leveraging AI for data insights can significantly impact business growth.

19. OpenAI: Transforming Natural Language Processing

Task/Conflict: OpenAI aimed to advance NLP by developing models capable of generating coherent and contextually relevant text, opening new possibilities in AI-human interaction.

Solution: OpenAI developed the Generative Pre-trained Transformer (GPT) models, which use deep learning to generate text that closely mimics human language. These models are trained on vast datasets, enabling them to understand context and generate responses in a conversational and coherent manner.

Pioneered advancements in natural language understanding and generation.
Expanded the possibilities for AI applications in communication.
AI’s ability to mimic human language has vast potential applications.
Advancements in NLP are crucial for improving AI-human interactions.

20. Siemens: Pioneering Industrial Automation with AI

Task/Conflict: Industrial automation seeks to improve productivity and efficiency in manufacturing processes. Siemens faced the challenge of optimizing these processes using AI to reduce downtime and enhance output quality.

Solution: Siemens employs AI-driven solutions for predictive maintenance and process optimization to reduce downtime in industrial settings. Additionally, AI optimizes manufacturing processes, ensuring quality and efficiency.

Increased productivity and reduced downtime in industrial operations.
Enhanced quality and efficiency in manufacturing processes.
AI is a key driver in the advancement of industrial automation.
Predictive analytics are crucial for maintaining efficiency in manufacturing.

Related: Top Books for Learning AI

21. Ford: Driving Safety Innovation with AI

Task/Conflict: Enhancing automotive safety and providing effective driver assistance systems are critical challenges in the auto industry. Ford aimed to leverage AI to improve vehicle safety features and assist drivers in real-time decision-making.

Solution: Ford integrated AI into its advanced driver assistance systems (ADAS) to provide features like adaptive cruise control, lane-keeping assistance, and collision avoidance. These systems use sensors and cameras to gather data, which AI processes to make split-second decisions that enhance driver safety and vehicle performance.

Improved safety features in vehicles, minimizing accidents and improving driver confidence.
Enhanced driving experience with intelligent assistance features.
AI can highly enhance safety in the automotive industry.
Real-time data processing and decision-making are essential for effective driver assistance systems.

22. HSBC: Enhancing Banking Security with AI

Task/Conflict: As financial transactions increasingly move online, banks face heightened risks of fraud and cybersecurity threats. HSBC needed to bolster its protective measures to secure user data and prevent scam.

Solution: HSBC employed AI-driven security systems to observe transactions and identify suspicious activities. The AI models analyze patterns in customer behavior and flag anomalies that could indicate fraudulent actions, allowing for immediate intervention. This helps in minimizing the risk of financial losses and protects customer trust.

Strengthened security measures and reduced incidence of fraud.
Maintained high levels of customer trust and satisfaction.
AI is critical in enhancing security in the banking sector.
Proactive fraud detection can prevent significant financial losses.

23. Unilever: Optimizing Supply Chain with AI

Task/Conflict: Managing a global supply chain involves complexities related to logistics, demand forecasting, and sustainability practices. Unilever sought to enhance its supply chain efficiency while promoting sustainability.

Solution: Unilever implemented AI to optimize its supply chain operations, from raw material sourcing to distribution. AI algorithms analyze data to forecast demand, improve inventory levels, and minimize waste. Additionally, AI helps in selecting sustainable practices and suppliers, aligning with Unilever’s commitment to environmental responsibility.

Enhanced efficiency and reduced costs in supply chain operations.
Better sustainability practices, reducing environmental impact.
AI can highly optimize supply chain management.
Integrating AI with sustainability initiatives can lead to environmentally responsible operations.

24. Spotify: Personalizing Music Experience with AI

Task/Conflict: In the competitive music streaming industry, providing a personalized listening experience is crucial for user engagement and retention. Spotify needed to tailor music recommendations to individual tastes and preferences.

Solution: Spotify utilizes AI-driven algorithms to analyze user listening habits, preferences, and contextual data to recommend music tracks and playlists. This personalization ensures that users are continually engaged and discover new music that aligns with their tastes, enhancing their overall listening experience.

Increased customer engagement and time spent on the platform.
Higher user satisfaction and subscription retention rates.
Personalized content delivery is key to user retention in digital entertainment.
AI-driven recommendations significantly enhance user experience.

Related: How can AI be used in Instagram Marketing?

25. Walmart: Revolutionizing Retail with AI

Task/Conflict: Retail giants like Walmart face challenges in inventory management and providing a high-quality customer service experience. Walmart aimed to use AI to optimize these areas and enhance overall operational efficacy.

Solution: Walmart deployed AI technologies across its stores to manage inventory levels effectively and enhance customer service. AI systems predict product demand to optimize stock levels, while AI-driven robots assist in inventory management and customer service, such as guiding customers in stores and handling queries.

Improved inventory management, reducing overstock and shortages.
Enhanced customer service experience in stores.
AI can streamline retail operations significantly.
Enhanced customer service through AI leads to better customer satisfaction.

26. Roche: Innovating Drug Discovery with AI

Task/Conflict: The pharmaceutical industry faces significant challenges in drug discovery, requiring vast investments of time and resources. Roche aimed to utilize AI to streamline the drug development process and enhance the discovery of new therapeutics.

Solution: Roche implemented AI to analyze medical data and simulate drug interactions, speeding up the drug discovery process. AI models predict the effectiveness of compounds and identify potential candidates for further testing, significantly minimizing the time and cost related with traditional drug development procedures.

Accelerated drug discovery processes, bringing new treatments to market faster.
Reduced costs and increased efficiency in pharmaceutical research.
AI can greatly accelerate the drug discovery process.
Cost-effective and efficient drug development is possible with AI integration.

27. IKEA: Enhancing Customer Experience with AI

Task/Conflict: In the competitive home furnishings market, enhancing the customer shopping experience is crucial for success. IKEA aimed to use AI to provide innovative design tools and improve customer interaction.

Solution: IKEA introduced AI-powered tools such as virtual reality apps that allow consumers to visualize furniture before buying. These tools help customers make more informed decisions and enhance their shopping experience. Additionally, AI chatbots assist with customer service inquiries, providing timely and effective support.

Improved customer decision-making and satisfaction with interactive tools.
Enhanced efficiency in customer service.
AI can transform the retail experience by providing innovative customer interaction tools.
Effective customer support through AI can enhance brand loyalty and satisfaction.

28. General Electric: Optimizing Energy Production with AI

Task/Conflict: Managing energy production efficiently while predicting and mitigating potential issues is crucial for energy companies. General Electric (GE) aimed to improve the efficiency and reliability of its energy production facilities using AI.

Solution: GE integrated AI into its energy management systems to enhance power generation and distribution. AI algorithms predict maintenance needs and optimize energy production, ensuring efficient operation and reducing downtime. This predictive maintenance approach saves costs and enhances the reliability of energy production.

Increased efficiency in energy production and distribution.
Reduced operational costs and enhanced system reliability.
Predictive maintenance is crucial for cost-effective and efficient energy management.
AI can significantly improve the predictability and efficiency of energy production.

Related: Use of AI in Sales

29. L’Oréal: Transforming Beauty with AI

Task/Conflict: Personalization in the beauty industry enhances customer satisfaction and brand loyalty. L’Oréal aimed to personalize beauty products and experiences for its diverse customer base using AI.

Solution: L’Oréal leverages AI to assess consumer data and provide personalized product suggestions. AI-driven tools assess skin types and preferences to recommend the best skincare and makeup products. Additionally, virtual try-on apps powered by AI allow customers to see how products would look before making a purchase.

Enhanced personalization of beauty products and experiences.
Increased customer engagement and satisfaction.
AI can provide highly personalized experiences in the beauty industry.
Data-driven personalization enhances customer satisfaction and brand loyalty.

30. The Weather Company: AI-Predicting Weather Patterns

Task/Conflict: Accurate weather prediction is vital for planning and safety in various sectors. The Weather Company aimed to enhance the accuracy of weather forecasts and provide timely weather-related information using AI.

Solution: The Weather Company employs AI to analyze data from weather sensors, satellites, and historical weather patterns. AI models improve the accuracy of weather predictions by identifying trends and anomalies. These enhanced forecasts help in better planning and preparedness for weather events, benefiting industries like agriculture, transportation, and public safety.

Improved accuracy in weather forecasting.
Better preparedness and planning for adverse weather conditions.
AI can enhance the precision of meteorological predictions.
Accurate weather forecasting is crucial for safety and operational planning in multiple sectors.

31. Cisco: Securing Networks with AI

Task/Conflict: As cyber threats evolve and become more sophisticated, maintaining robust network security is crucial for businesses. Cisco aimed to leverage AI to enhance its cybersecurity measures, detecting and responding to threats more efficiently.

Solution: Cisco integrated AI into its cybersecurity framework to analyze network traffic and identify unusual patterns indicative of cyber threats. This AI-driven approach allows for real-time threat detection and automated responses, thus improving the speed and efficacy of security measures.

Strengthened network security with faster threat detection.
Reduced manual intervention by automating threat responses.
AI is essential in modern cybersecurity for real-time threat detection.
Automating responses can significantly enhance network security protocols.

32. Adidas: AI in Sports Apparel Manufacturing

Task/Conflict: To maintain competitive advantage in the fast-paced sports apparel market, Adidas sought to innovate its manufacturing processes by incorporating AI to improve efficiency and product quality.

Solution: Adidas employed AI-driven robotics and automation technologies in its factories to streamline the production process. These AI systems optimize manufacturing workflows, enhance quality control, and reduce waste by precisely cutting fabrics and assembling materials according to exact specifications.

Increased production efficacy and reduced waste.
Enhanced consistency and quality of sports apparel.
AI-driven automation can revolutionize manufacturing processes.
Precision and efficiency in production lead to higher product quality and sustainability.

Related: How can AI be used in Disaster Management?

33. KLM Royal Dutch Airlines: AI-Enhanced Customer Service

Task/Conflict: Enhancing the customer service experience in the airline industry is crucial for customer satisfaction and loyalty. KLM aimed to provide immediate and effective assistance to its customers by integrating AI into their service channels.

Solution: KLM introduced an AI-powered chatbot, which provides 24/7 customer service across multiple languages. The chatbot handles inquiries about flight statuses, bookings, and baggage policies, offering quick and accurate responses. This AI solution helps manage customer interactions efficiently, especially during high-volume periods.

Improved customer service efficiency and responsiveness.
Increased customer satisfaction through accessible and timely support.
AI chatbots can highly improve user service in high-demand industries.
Effective communication through AI leads to better customer engagement and loyalty.

34. Novartis: AI in Drug Formulation

Task/Conflict: The pharmaceutical industry requires rapid development and formulation of new drugs to address emerging health challenges. Novartis aimed to use AI to expedite the drug formulation process, making it faster and more efficient.

Solution: Novartis applied AI to simulate and predict how different formulations might behave, speeding up the lab testing phase. AI algorithms analyze vast amounts of data to predict the stability and efficacy of drug formulations, allowing researchers to focus on the most promising candidates.

Accelerated drug formulation and reduced time to market.
Improved efficacy and stability of pharmaceutical products.
AI can significantly shorten the drug development lifecycle.
Predictive analytics in pharmaceutical research can lead to more effective treatments.

35. Shell: Optimizing Energy Resources with AI

Task/Conflict: In the energy sector, optimizing exploration and production processes for efficiency and sustainability is crucial. Shell sought to harness AI to enhance its oil and gas operations, making them more efficient and less environmentally impactful.

Solution: Shell implemented AI to analyze geological data and predict drilling outcomes, optimizing resource extraction. AI algorithms also adjust production processes in real time, improving operational proficiency and minimizing waste.

Improved efficiency and sustainability in energy production.
Reduced environmental impact through optimized resource management.
Automation can enhance the effectiveness and sustainability of energy production.
Real-time data analysis is crucial for optimizing exploration and production.

36. Procter & Gamble: AI in Consumer Goods Production

Task/Conflict: Maintaining operational efficiency and innovating product development are key challenges in the consumer goods industry. Procter & Gamble (P&G) aimed to integrate AI into their operations to enhance these aspects.

Solution: P&G employs AI to optimize its manufacturing processes and predict market trends for product development. AI-driven data analysis helps in managing supply chains and production lines efficiently, while AI in market research informs new product development, aligning with consumer needs.

Enhanced operational efficacy and minimized production charges.
Improved product innovation based on consumer data analysis.
AI is crucial for optimizing manufacturing and supply chain processes.
Data-driven product development leads to more successful market introductions.

Related: Use of AI in the Navy

37. Disney: Creating Magical Experiences with AI

Task/Conflict: Enhancing visitor experiences in theme parks and resorts is a priority for Disney. They aimed to use AI to create personalized and magical experiences for guests, improving satisfaction and engagement.

Solution: Disney utilizes AI to manage park operations, personalize guest interactions, and enhance entertainment offerings. AI algorithms predict visitor traffic and optimize attractions and staff deployment. Personalized recommendations for rides, shows, and dining options enhance the guest experience by leveraging data from past visits and preferences.

Enhanced guest satisfaction through personalized experiences.
Improved operational efficiency in park management.
AI can transform the entertainment and hospitality businesses by personalizing consumer experiences.
Efficient management of operations using AI leads to improved customer satisfaction.

38. BMW: Reinventing Mobility with Autonomous Driving

Task/Conflict: The future of mobility heavily relies on the development of safe and efficient autonomous driving technologies. BMW aimed to dominate in this field by incorporating AI into their vehicles.

Solution: BMW is advancing its autonomous driving capabilities through AI, using sophisticated machine learning models to process data from vehicle sensors and external environments. This technology enables vehicles to make intelligent driving decisions, improving safety and passenger experiences.

Pioneering advancements in autonomous vehicle technology.
Enhanced safety and user experience in mobility.
AI is crucial for the development of autonomous driving technologies.
Safety and reliability are paramount in developing AI-driven vehicles.

39. Mastercard: Innovating Payment Solutions with AI

Task/Conflict: In the digital age, securing online transactions and enhancing payment processing efficiency are critical challenges. Mastercard aimed to leverage AI to address these issues, ensuring secure and seamless payment experiences for users.

Solution: Mastercard integrates AI to monitor transactions in real time, detect fraudulent activities, and enhance the efficiency of payment processing. AI algorithms analyze spending patterns and flag anomalies, while also optimizing authorization processes to reduce false declines and improve user satisfaction.

Strengthened security and reduced fraud in transactions.
Improved efficiency and user experience in payment processing.
AI is necessary for securing and streamlining expense systems.
Enhanced transaction processing efficiency leads to higher customer satisfaction.

40. AstraZeneca: Revolutionizing Oncology with AI

Task/Conflict: Advancing cancer research and developing effective treatments is a pressing challenge in healthcare. AstraZeneca aimed to utilize AI to revolutionize oncology research, enhancing the development and personalization of cancer treatments.

Solution: AstraZeneca employs AI to analyze genetic data and clinical trial results, identifying potential treatment pathways and personalizing therapies based on individual genetic profiles. This approach accelerates the development of targeted treatments and improves the efficacy of cancer therapies.

Accelerated innovation and personalized treatment in oncology.
Better survival chances for cancer patients.
AI can significantly advance personalized medicine in oncology.
Data-driven approaches in healthcare lead to better treatment outcomes and innovations.

Related: How can AI be used in Tennis?

Closing Thoughts

These 40 case studies illustrate the transformative power of AI across various industries. By addressing specific challenges and leveraging AI solutions, companies have achieved remarkable outcomes, from enhancing customer experiences to solving complex scientific problems. The key learnings from these cases underscore AI’s potential to revolutionize industries, improve efficiencies, and open up new possibilities for innovation and growth.

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Artificial intelligence (AI) has become increasingly prevalent in business as companies adopt machine algorithms that can learn and improve over time. These AI-powered solutions are used in various business areas, including operations, analytics, product personalisation, marketing, sales, customer service, and human resource (HR). AI can help companies automate mundane tasks, make smarter decisions based on data and insights, and provide capabilities for smoother customer experiences, better customer service, and increased efficiency. AI has also allowed companies to enhance the quality of their digital services, optimise supply chain processes, and gain access to real-time insights and analytics. Companies can use AI to reduce lead times, generate new customer insights, improve customer service, and create meaningful customer experiences. This paper aims to address the gap in knowledge on incorporating AI into business strategy by conducting a critical literature review, synthesisi...

Dr.Sharif Uddin Ahmed Rana

Artificial Intelligence (AI) has become a critical technology in business. It has the potential to improve business efficiency, reduce costs, and enhance customer experience. However, businesses face various challenges in implementing AI, such as data privacy concerns, lack of skills, and regulatory issues. This research proposal aims to study the benefits, challenges, and future prospects of AI application in business. The research will be conducted using a mixed-methods approach, including a survey questionnaire and interviews. The expected outcomes of the study are a comprehensive understanding of the benefits and challenges of AI application in business, identification of the factors that affect the implementation of AI in business and an analysis of the future prospects of AI application in business. The findings of the study will be beneficial to businesses that are considering implementing AI and policymakers who are responsible for regulating the use of AI.

Journal of Engineering Research and Reports

JASMIN BHARADIYA

The ongoing development of business and the most recent advances in artificial intelligence (AI) allow for the many business practices to be improved by the capacity to establish new forms of collaboration, which is a significant competitive advantage. This rapidly developing technology enables to offer brand services and even some new forms of business interactions with consumers and personnel. The digitalization of AI concurrently emphasized for businesses that they need concentrate on their present strategies while also routinely and early pursuing new chances in the market. Not only in business but also in different industry sectors, Al techniques are being used and revolutionized different industry sectors. This review focuses on the application of AI techniques in business and different industries.

IAEME PUBLICATION

IAEME Publication

Speed and quality are the mantras for today’s business world and Artificial Intelligence has marked as significant game-changer in these in all business organizations. As businesses are becoming global and the future is more of machinebased work than human-based. Irrespective of the business volume, the new culture to adapt to AI machinery is a prominent need than a comfort. The trend is challenging the change itself. AI plays a significant role and becomes mandatory in business sustainability. Many of the businesses have become early adopters of AI and making the transactions esp. the customer service with FAQ at the top most importance. Every department in the business organization tries to implement and become best service providers for their customer questions. In this paper, the effect of AI in the business organizations and how the individual departments benefit from it is discussed and explained

International Journal of Engineering Research and Advanced Technology (IJERAT)

Editor Ijasre , Sarhan M. Musa

Artificial intelligence (AI) is a field of computer science that is dedicated to developing software dealing with intelligent decisions, reasoning, and problem solving. Artificial intelligence is already part of our lives, slowly shaping our society and business. It is everywhere, in on your smartphones, laptops, and cars. AI can increase productivity, gain competitive advantage, compliment human intelligence. and reduce cost of operations. Businesses of all types and sizes are considering artificial intelligence to solve their problems. The scope of AI in business transformation is constantly growing. This paper provides an introduction on the applications of AI in business.

RESEARCH REVIEW International Journal of Multidisciplinary

The development of Artificial Intelligence is speeding up rapidly and combination of Artificial Intelligence with automation has started to change the business landscape. Companies and business are focusing on applying existing Artificial Intelligence with automation processes to gain the new heights of efficiency and quality. The paper depicts about artificial intelligence and automation, and it tries to demonstrate the audience how both Artificial intelligence and automation are related and how they can be more effective when they work together and can give competitive advantage.

International Journal of Computer Trends and Technology (IJCTT)

sikender mohsienuddin mohammad

Artificial intelligence, commonly referred to as AI, has, in recent years, transformed the nature of human life in almost every aspect that is connected to human life. The aspects include economic status, job creation, and employment, communication, war, security, privacy, and healthcare. The long term evolution of AI is yet to be seen if it will lead humanity in making the world a better place for living in or a disastrous place. For every technology to survive in the market, the advantages must outdo the disadvantages since every technology has its advantages and disadvantages. In artificial intelligence, since we have not yet reached the long term evolution, we are, however, to see if it will have an impact on the positive effects than the adverse effects. In the world today, we are surrounded by technology in every aspect of life, and we seem to embrace them starting from the aspect of healthcare, industries, smart homes, and even cars that are autonomous. On the negative side, the technology seems to take away jobs from many individuals who are tot intellect on matters technology hence creating the context of unemployment. As technology is advancing quickly, robots and autonomous systems are developed and born daily, therefore replacing the labor provided by humans. However, this being the current situation generated by technology, the exciting parts are brought by the system's long term results which tend to be very fulfilling to the human life. Throughout this essay, I will address the Artificial Intelligence starting from its development to the current situation, including its significance in human life in both the positive aspects and negative aspects of life.

David George

Written for business leaders and general management the observations and discussion points in the main body of the article are non-technical and provide insights informing the reader about some of the business applications of Applied Artificial Intelligence (AI) and the on-going research into the more ambitious Artificial General Intelligence (AGI). The AI technology space is poised for significant growth as robust and proven solutions emerge from academia and private development laboratories. AI is becoming pervasive as the high-tech industry invests increasingly significant funds to develop and incorporate it as component parts of their consumer and other business offerings as the means of making products and services ‘smarter’. There will be an impact on how individuals, business, and societies operate and interact with each other. How we utilise computers and other increasingly intelligent products and automated online Ecommerce services will change. Our purchasing behaviour will be transformed as a direct result of new methods used to target and influence how we choose and make buying decisions. The longer-term research goals of attaining human-like cognitive abilities are far reaching, complex, complicated, and extremely challenging; indeed some of the goals may never be achieved. There are implications, and some difficult questions will require considered answers. However, given today’s economic circumstances and the corresponding demands on business, AI provides tools to create opportunities supporting increased business process efficacy, product and service level enhancements, differentiation, and can assist business with gaining and maintaining a necessary competitive advantage in a relatively uncertain and dynamic future.

Knowledge Engineering Review

Artificial intelligence test: a case study of intelligent vehicles

Published: 12 April 2018
Volume 50 , pages 441–465, ( 2018 )

Cite this article

Yi-Lun Lin 2 , 5 ,
Nan-Ning Zheng 3 ,
Fei-Yue Wang ORCID: orcid.org/0000-0001-9185-3989 2 , 5 ,
Yuehu Liu 3 ,
Dongpu Cao 4 , 6 ,
Kunfeng Wang 2 &
Wu-Ling Huang 2

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To meet the urgent requirement of reliable artificial intelligence applications, we discuss the tight link between artificial intelligence and intelligence test in this paper. We highlight the role of tasks in intelligence test for all kinds of artificial intelligence. We explain the necessity and difficulty of describing tasks for intelligence test, checking all the tasks that may encounter in intelligence test, designing simulation-based test, and setting appropriate test performance evaluation indices. As an example, we present how to design reliable intelligence test for intelligent vehicles. Finally, we discuss the future research directions of intelligence test.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 91520301 and 61533019, and the Beijing Municipal Science and Technology Project (No. D171100000317002).

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Department of Automation, BNRist, Tsinghua University, Beijing, 100084, China

The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

Yi-Lun Lin, Fei-Yue Wang, Kunfeng Wang & Wu-Ling Huang

Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, 710049, China

Nan-Ning Zheng & Yuehu Liu

Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, N2L3G1, Canada

Qingdao Academy of Intelligent Industries, Qingdao, 266109, Shandong, China

Yi-Lun Lin & Fei-Yue Wang

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Li, L., Lin, YL., Zheng, NN. et al. Artificial intelligence test: a case study of intelligent vehicles. Artif Intell Rev 50 , 441–465 (2018). https://doi.org/10.1007/s10462-018-9631-5

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DOI : https://doi.org/10.1007/s10462-018-9631-5

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AI Is Making Economists Rethink the Story of Automation

Walter Frick

Economists have traditionally believed that new technology lifts all boats. But in the case of AI, some are asking: Will some employees get left behind?

Will artificial intelligence take our jobs? As AI raises new fears about a jobless future, it’s helpful to consider how economists’ understanding of technology and labor has evolved. For decades, economists were relatively optimistic, and pointed out that previous waves of technology had not led to mass unemployment. But as income inequality rose in much of the world, they began to revise their theories. Newer models of technology’s affects on the labor market account for the fact that it absolutely can displace workers and lower wages. In the long run, technology does tend to raise living standards. But how soon and how broadly? That depends on two factors: Whether technologies create new jobs for people to do and whether workers have a voice in technology’s deployment.

Is artificial intelligence about to put vast numbers of people out of a job? Most economists would argue the answer is no: If technology permanently puts people out of work then why, after centuries of new technologies, are there still so many jobs left ? New technologies, they claim, make the economy more productive and allow people to enter new fields — like the shift from agriculture to manufacturing. For that reason, economists have historically shared a general view that whatever upheaval might be caused by technological change, it is “somewhere between benign and benevolent.”

Walter Frick is a contributing editor at Harvard Business Review , where he was formerly a senior editor and deputy editor of HBR.org. He is the founder of Nonrival , a newsletter where readers make crowdsourced predictions about economics and business. He has been an executive editor at Quartz as well as a Knight Visiting Fellow at Harvard’s Nieman Foundation for Journalism and an Assembly Fellow at Harvard’s Berkman Klein Center for Internet & Society. He has also written for The Atlantic , MIT Technology Review , The Boston Globe , and the BBC, among other publications.

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http://orcid.org/0009-0008-5169-5857 Yueye Wang 1 ,
Chi Liu 2 ,
Keyao Zhou 3 , 4 ,
Tianqing Zhu 2 ,
http://orcid.org/0000-0001-6836-3447 Xiaotong Han 1
1 Sun Yat-sen University Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology , Guangzhou , Guangdong , China
2 Faculty of Data Science, City University of Macau , Macao SAR , China
3 Department of Ophthalmology , Guangdong Provincial People's Hospital , Guangzhou , Guangdong , China
4 Department of Neurosurgery , Huashan Hospital, Fudan University , Shanghai , China
Correspondence to Dr Xiaotong Han, Sun Yat-Sen University Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology, Guangzhou, Guangdong, China; lh.201205{at}aliyun.com ; Dr Chi Liu, Faculty of Data Science, City University of Macau, Macao SAR, China; chiliu{at}cityu.edu.mo

As the healthcare community increasingly harnesses the power of generative artificial intelligence (AI), critical issues of security, privacy and regulation take centre stage. In this paper, we explore the security and privacy risks of generative AI from model-level and data-level perspectives. Moreover, we elucidate the potential consequences and case studies within the domain of ophthalmology. Model-level risks include knowledge leakage from the model and model safety under AI-specific attacks, while data-level risks involve unauthorised data collection and data accuracy concerns. Within the healthcare context, these risks can bear severe consequences, encompassing potential breaches of sensitive information, violating privacy rights and threats to patient safety. This paper not only highlights these challenges but also elucidates governance-driven solutions that adhere to AI and healthcare regulations. We advocate for preparedness against potential threats, call for transparency enhancements and underscore the necessity of clinical validation before real-world implementation. The objective of security and privacy improvement in generative AI warrants emphasising the role of ophthalmologists and other healthcare providers, and the timely introduction of comprehensive regulations.

Public health

Data availability statement

Data sharing not applicable as no data sets generated and/or analysed for this study. Not applicable.

https://doi.org/10.1136/bjo-2024-325167

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Introduction

Over the past decade, the field of medicine has grasped opportunities from artificial intelligence (AI). 1 2 There is growing optimism regarding the promise of this technology to transform healthcare. 3 The image-centric nature of ophthalmology renders the development and application of AI within this field tremendous progress. 4 While alongside its huge potential, security and privacy issues arising from AI are of paramount importance. The powerful capabilities and extensive data consumption behind the AI raise concerns that without proper development and regulation, this technology may present risks and potential harm to individual privacy and security. 5 Nowhere are these concerns more critical than in the healthcare industry, where data leakage or model misuse can have severe consequences. To ensure the responsible use of this technology for the public’s benefit, the European Union (EU) published the first AI-specific regulation in 2021—the EU AI Act and other regulatory institutions like WHO and Food and Drug Administration (FDA) are catching up in recent years. 6–8

Generative AI, which enables the creation of various synthetic contexts based on users’ input, can highly speed up information translation and technological innovation. 9 Large language models (LLM), such as ChatGPT and Bard, showed great performance in the ophthalmology question–answering domain, even superior to historical human marks. 10 11 However, as we embrace the wave of generative AI, we are just starting to grasp the risks that come with it. Differing from regulated AI, generative AI presents unique characteristics such as extensive training data, broad applications, interactive data flow and synthetic contents. These features can pose new challenges that current regulations have not fully addressed. The extensive and diverse use of large generative AI models necessitates specific guidelines to protect security and privacy. 12

This paper aims to discuss the security and privacy challenges of generative AI in the context of healthcare. We present vulnerabilities at both the model and data levels, argue the consequences of such risk in ophthalmology and the broader medical fields and propose solutions and regulatory approaches to harness the full potential of this technology.

Security and privacy risks

Model-level risk, knowledge leakage.

The development of large generative models requires substantial data, expert knowledge, computational resources and trial and error, leading to substantial expenses for the developers. This is especially pronounced in the medical domain where data is highly sensitive and difficult to collect, coupled with the elevated costs for expert knowledge acquisition. Therefore, well-trained models are valuable digital assets whose safety should be protected. Traditionally, due to the ‘black box’ nature of AI, it is assumed that a deployed model is complicated to penetrate and reproduce without explicit knowledge of model architecture, training set and parameters. However, attacks like reverse engineering, model inversion attack and model extraction attack can still put the trained model at risk of being stolen during the inference time. 13–15 In the case of generative AI, network architecture and model hyperparameters can be inferred from the outputs (eg, synthetic images generated by the model). 16

Data used for training a generative AI model is also at risk of leakage. A membership inference attack is a way to expose the training data only from the queried outputs of a model. 17 For example, by deducing the participation of individuals in the training set from the output of a generative model, up to over 80% of the training set data can be inferred under a membership inference attack. 18 The risk of data leakage is amplified in generative models than in traditional medical AI models such as image-based classification models (eg, diagnostic models for eye diseases). This is because traditional models merely output abstract decisions based on prediction probabilities, and thus attackers are only able to infer approximate distributional information of training set with particular attacks. In contrast, generative models directly generate exact data points that belong to the same distribution of the training set, wherein the original training knowledge may leak spontaneously. For instance, with deliberate prompt decoys, an LLM can disclose the texts it has learnt in its responses. 19

Model corruption

Given the heightened expectations regarding AI’s role in supporting clinical decision-making, the integrity and reliability of the models should be prerequisites for healthcare practices. Unfortunately, current AI techniques, including generative AI, are vulnerable to orchestrated attacks. For instance, adversarial attacks, test-time attacks that perturb a clean test sample with imperceptible adversarial noises, can misguide a well-trained AI model to give a wrong prediction. 20 Another typical attack is known as a poisoning attack, where an attacker intentionally manipulates the training data of an AI model when they gain access to the training data set. The model trained with the poisoned data set becomes corrupted and, consequently would make inaccurate or biased predictions controlled by the triggering signals from the attacker during inference. 21

Model corruption can be especially impactful for generative AI, given the increasing reliance on it as a primary source of medical information. A corrupted model could be manipulated to generate toxic, biased or misleading content. What is worse, such problematic outputs are harder for humans to identify than detecting incorrect predictions from traditional medical AI, since LLMs present information interactively in natural language. Additionally, common AI attack vectors like adversarial attacks, backdoors and data poisoning are shifting focus from training-time attacks to prompt-level and fine-tuning attacks that no longer require data set access. This reduces attack costs, expanding and diversifying attack surfaces, necessitating urgent research attention.

Data-level risk

Unauthorised data collection.

Numerous data privacy breaches involving AI have been reported in the healthcare sector. 22 In the era of generative AI, the risk of healthcare data privacy violation has been significantly aggravated, as the information channels are greatly expanded through human-AI interaction. The Health Insurance Portability and Accountability Act calls for attention to the heightened risk of healthcare data breaches due to generative AI. 23 For example, synthetic content from generative AI can abused for healthcare fraud. Additionally, conversational AI chatbots released by unauthorised services can be manipulated to steal patients’ data. The risk is particularly noteworthy in the field of ophthalmology, as the eye is usually considered a window to the body. Even from the input of a single retinal image, one’s demographic characteristics (eg, age, sex) and systematic conditions (eg, circulatory, neurological ageing and diseases) would be at risk of leakage.

Notably, LLMs like ChatGPT involve reinforcement learning from human feedback, which by default uses user-entered information for continuous training. 24 This poses an unwitting risk of exposing sensitive data at various stages of generative AI use. When being implemented for medical use, the models may receive sensitive prompts such as personal information and medical conditions without explicit and proper consent obtained from patients, which might violate the users’ original purpose.

Is seeing always believing?

Generative AI can synthesise content that looks highly realistic. For instance, a diffusion model can perform text-to-image translation, yielding high-fidelity output. 25 A generative adversarial network also makes it possible for transmodality from a fundus photograph to retinal angiography images with explicit microvasculature details. 26 However, it is essential to acknowledge that in medical practice, accuracy is critical besides fidelity. Content created by generative AI can have mistakes that do not align with science. 27 Another concern is the use of LLMs as medical databases; LLMs may provide persuasive yet fake citations when asked about data sources. 5

Unfortunately, clinical validation for generative AI is extremely challenging right now, primarily due to the massive content the model can generate. This unpredictability hinders generative models from meeting the regulation standards of the FDA as medical devices. Although we already have models that can generate fundus images featuring detailed retinal lesions, ascertaining the reliability of these lesions poses a challenge. The clinical application of such generative models also requires further validation.

Consequences in healthcare practice and case studies

Patient information breaches.

Generative AI has shown its value to be deployed in enhancing clinical documentation and workflow, supporting decision-making and improving interaction with patients. 12 In the diagnosis of ophthalmological diseases and in generating proficient operative notes, LLMs showed parallel performance to ophthalmology trainees or interns, indicating a promising way to reduce ophthalmologist workload. 28 However, as a potential consequence of relying on these models, the inevitable risk of sensitive information breach, such as biological data or clinical records, can severely violate patients’ privacy. The leading LLM, ChatGPT, experienced its first data breach on 20 March this year, affecting approximately 1.2 million users with exposed data. 29 This incident resulted from a bug in the open-source code, enabling some users to access others’ personal information (name, address, payment information and chat history).

This situation underscores the high risk regarding the application of generative models in clinical workflows. General LLMs, particularly cloud-based models, may harbour massive and dynamic patient information, making them vulnerable to potential breaches of sensitive data. To address this concern, one mitigation approach is to employ a locally customised LLM (such as Microsoft’s AutoGen), not relying on third-party servers. This strategy ensures that sensitive patient data remains confidential and secure within the context of the local health system. One alternative is to use a dedicated service such as Azure OpenAI services, where patient data is not available to be copied to the LLM or other customers or models, minimising the risk of data breaches.

Moreover, if malicious users manage to retrieve the training data or even the generative model itself through leaked information, the model’s integrity can be compromised. Recently, researchers from Google’s DeepMind used only simple prompts and extracted private training data from ChatGPT, breaking the model’s safeguards. 30 Any response lag in generative AI due to such a model attack may result in delayed patient management. Additionally, incorrect output generated from a corrupted model may contribute to misdiagnosis and inappropriate treatments.

Violation of the ‘Right to be Forgotten’

The ‘right to be forgotten’ under Article 17 of the General Data Protection Regulation (GDPR) is an essential consideration for human privacy. It addresses the need to mitigate prolonged storage of private medical information, which can be risky. This rule also acknowledges patients’ entitlement to request the removal of their data from AI models. Nevertheless, generative AI, especially LLMs, confronts obstacles in complying with this rule. The model requires massive amounts of clinical data for training, while the source of the training data is essentially unknown to both clinicians and patients. Even if removing personal data from the training data set, the trained model can produce outputs containing patient-specific information. In one case, researchers showed that ChatGPT provided the personal information of individual A in a request for a non-related individual B. 31 This case proved that LLM can memorise personal data and generate this data in future output.

Prolonged data retention or the failure to delete data on request may heighten the risk of unauthorised access or the unintended use of sensitive personal information. Even the training interval of Llama 2 family, the LLMs from Meta, was only around 2 months, this timespan still failed the requirement of GDPR’s ‘undue delay’—around 1 month. Lack of transparency is another critical concern in the use of generative AI that may harm the ‘right to be forgotten’. Patients entrust their personal data to healthcare providers and have the right to comprehensive information about data collection, processing and storage. Unfortunately, existing data processing disclosures have proven inadequate in this regard.

Is synthetic data safe for patient?

In digital modelling for trends assessment and prediction of disease risks, using synthetic data can augment sample sizes or enhance data set diversity, overcome challenges posed by insufficient real-world data or simulation on rare conditions. A recent study adopted Synthea, a well-documented and peer-reviewed generative AI to generate a 1.2 million Massachusetts patient cohort. 32 This study indicated that high-fidelity synthetic data performed well in modelling demographics, showing promise as a valuable analytics tool for aiding decision-making processes. However, it is noteworthy that synthetic data may not act well when simulating positive cases or reflecting disease-related metrics like mortality rates or complication rates. It is also challenging for synthetic data to monitor specific trends related to novel treatments or patient prognoses. This limitation of synthetic data may result in an underestimation of actual clinical outcomes.

Synthetic information from LLMs may also not be suitable for real-world medical use. Though studies are showing LLM can pass medical examinations such as the US Medical Licensing Examination. 33 Healthcare providers still express major concerns about accuracy and reliability regarding synthetic answers. 34 In the case of ChatGPT, it was found able to manage appropriately in retinal diseases, yet showed significant mistakes in other subdomain diseases such as lacrimal drainage disorders and anterior ischaemic optic neuropathy. 35–37

Based on the current evidence, synthetic data may not yet be a reliable proxy for real patient data. Inaccurate synthetic data may yield misleading or biased outcomes in medical research and practice, thereby exacerbating the potential for unsafe clinical management. 38 Before the clinical application of synthetic data, it is imperative to ensure its comparability to real-world data, safeguarding against potential harm to patients. Looking ahead, researchers are optimistic about this cutting-edge tool but stress the need for rigorous external clinical validation and accurate information.

Solutions under regulation

Be prepared for the attack.

In the latest updates, the first regulation on AI, the EU AI Act requires providers of generative AI to deploy the model with adequate training and design safeguards (Article 28). Prior to deployment in real clinical practice, anti-attack techniques such as adversarial training and robust model validation can help identify and mitigate model-level attacks. 39 The German Federal Office for Information and Security suggested several ways to reduce vulnerability to model attacks, 40 including avoiding identifiable and sensitive data, specifying training on sensitive data, restricting the user group, etc. In short, model providers and users need to be aware of the potential risk of model attacks and prepare plans for these attacks, such as model pre-training and user training.

Informed consent, data share and transparency

When using generative AI, it is vital for both model providers and users to comply with GDPR guidelines regarding lawful personal data collection and processing. This includes obtaining explicit consent from patients, providing transparent information about how data will be used, ensuring data security and enabling patients to exercise their data-related rights. Healthcare organisations must diligently train their staff to ensure compliance with patient security and privacy laws before deploying generative AI tools. Also, decentralisation technologies like blockchain, and federated learning, can be integrated with generative AI to enhance security in data share and storage.

Regarding the transparency of AI tools, research from Stanford University highlights that major providers of foundational models in the market have not strictly complied with the EU’s regulations. 41 Model providers may be reluctant to adhere strictly to transparency regulations, citing concerns related to both competitive factors and security considerations. To address these concerns and enhance transparency, regulators should also provide appropriate safeguards and guidance to model providers when regulating their activities.

Clinical validation

EU AI law mandates that synthetic content generation must adhere to legal requirements. However, within the healthcare context, legality alone is insufficient. Authentication and accuracy should also be put on the table. The Data Protection and Privacy Authorities (DPA) G7 underscores the importance of meticulously examining the interaction of individuals with generative AI and the processing of the content generated by generative AI tools. Cases with the data of minors should gain specific attention. Given that current models are typically trained on data from the general population, clinicians need to take care to determine whether a patient belongs to a vulnerable group or has characteristics that might make the model’s use inappropriate, to ensure patient safety.

Currently, clinical evaluations of LLMs primarily focus on accuracy. 42 To justify the use of LLMs for clinical applications, it is also essential to define other appropriate procedures and endpoints beyond effectiveness evaluation. Robust protocols are required to address security and privacy concerns. Before data is fed into the LLM, it is crucial to assess the success rate of pre-processing procedures, such as replacing, masking or anonymising to remove any personally identifiable information. Continuous monitoring of incoming requests in real-time helps to identify any instances where personally identifiable information reaches the LLM. This ensures that the LLM is working with data that has been thoroughly sanitised. In addition, the output generated by an LLM needs to be thoroughly evaluated against standard clinical practices. Content filtering and abuse monitoring can help evaluate every decision made by an LLM to detect and mitigate instances of harmful content generation. The most important part is clinician involvement in the quality appraisal process, as the unsupervised deployment of LLMs is considered impractical. 43 Clinicians need to evaluate whether the output of LLM is harmful to patients, may disclose individual information and introduce bias or discrimination into downstream clinical decisions.

Future perspectives

‘Many of the problems caused by AI can also be managed with the help of AI’, as said by Bill Gates. 44 During model development, data protection should be integrated into the system. DPA G7 suggested generative AI models to embed data protection throughout the product total lifecycle, based on the concept of ‘Privacy by Design’. Yang et al proposed using a digital mask to erase identifiable features while preserving the main features for ocular condition diagnosis. 45 By constructing synthetic data that behaves similarly to real clinical data, this privacy-protecting approach can be further enhanced with the help of generative AI. 45 46

The upcoming trend involves pre-trained large foundational models that serve as the cornerstone for various downstream AI applications. Two foundation models in ophthalmology, RETFound and VisionFM, have undergone training on millions of retinal images. They have effectively learnt general features from unlabeled data, showcasing remarkable capabilities in downstream tasks related to diagnosing ocular and systemic diseases. 47 48 The interaction of generative AI and the large foundation model may introduce new threats. For example, the frequently used pre-trained self-supervised learning encoder in foundational model techniques is susceptible to model stealing attacks. 49 These attacks not only endanger the model’s intellectual property but also potentially act as foreshadowing for subsequent attacks.

Current regulations are inadequate to address AI-related security and privacy concerns as they have already fallen outside the scope of national regulation for patient information protection. 50 Without expanding the regulatory framework, keeping up with the evolving landscape of generative AI can be difficult. Future regulatory perspectives may involve classifying AI software as medical devices, as the FDA has done, or shifting the regulatory focus from the AI models themselves to the providers, as the EU has pursued. Healthcare providers are the first line of defence for patient security and privacy. Generative AI and foundational models are ultimately tools designed to support healthcare services. It is important for future regulations to emphasise education for first-line healthcare service providers, on the responsible use of patient data to mitigate risks while maintaining accessibility and efficacy.

The full potential of generative AI technology can only be realised under effective governance. The principle of technology neutrality, where no specific technology is favoured or imposed, becomes crucial in the global competition to establish timely regulations for the burgeoning field of generative AI. There have been calls that AI regulations such as the EU AI Act need to take measures for sustainable development and innovation. 51 The Cyberspace Administration of China encourages innovation in generative AI services and lends support to the development of experimental research, while simultaneously emphasise responsibility for security and privacy. In the most recent recommendations from WHO, regulatory sandboxes are getting popular among regulators. 52 The Spanish government has approved the first AI regulatory sandbox pilot in 2023 as it allows the flexibility to test innovative products or services with minimal regulatory requirements and may foster AI innovation. 53 Achieving a trade-off between the advantages and risks associated with generative AI presents a tangible challenge for regulatory bodies in the near future.

Generative AI holds the potential to facilitate various aspects of clinical practice in ophthalmology and other healthcare domains. Prior to the implementation of this technology, it is essential to recognise the potential security and privacy risks associated with these models. Susceptible to both model-level and data-level attacks, generative models may cause severe consequences like extensive breaches of personal data, violation of patients’ privacy rights and safety. To address these concerns, it is crucial for model providers and users to proactively develop strategies that counteract attacks, enhance transparency and ensure robust validation throughout the entire product lifecycle, thus aligning with existing regulations. Furthermore, it is imperative to update the regulatory framework to keep pace with the ever-evolving landscape of generative AI, striking a balance between effective regulation and continued innovation.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

Acknowledgments.

We would like to acknowledge Professor Mingguang He and his research team for their kind suggestions during this study.

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YW and CL are joint first authors.

Contributors YW and CL developed the concept of the manuscript. YW, CL and KZ drafted the manuscript. XH and TZ made critical revisions of the manuscript. XH and TZ supervised the entire work. All authors approved the completed version. We used ChatGPT-4 only for proofreading and grammar checking of this review, but have not relied on the tool for any thinking or material presentation in this paper.

Funding This study was funded by the National Natural Science Foundation of China (82000901, 82101171), the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology (303060202400201209), the Outstanding PI Research Funds of the State Key Laboratory of Ophthalmology (3030902113083).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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