research topics on veterinary

Veterinary Medicine Research Paper Topics

Veterinary medicine research paper topics encompass a wide range of subjects that contribute to the advancement of animal healthcare. This page provides a comprehensive guide for students studying veterinary medicine who are tasked with writing research papers. Explore the intricacies of this field, delve into diverse categories, and discover a multitude of compelling topics to delve into. Whether you’re interested in animal behavior, infectious diseases, pharmacology, or veterinary surgery, this guide will help you navigate the realm of veterinary medicine research paper topics. By offering expert advice on topic selection and providing valuable insights on how to write an impactful research paper, we aim to empower students to make significant contributions to the field of veterinary medicine. Furthermore, iResearchNet’s writing services ensure that students receive top-quality, customized research papers tailored to their unique requirements. Let us help you unleash your academic potential and make a lasting impact in the world of veterinary medicine.

100 Veterinary Medicine Research Paper Topics

Introduction: The field of veterinary medicine encompasses a vast array of disciplines and areas of study, offering a wealth of research opportunities for students. This comprehensive list of veterinary medicine research paper topics is divided into 10 categories, each containing 10 unique topics. By exploring these topics, students can gain a deeper understanding of various aspects of veterinary medicine and contribute to the advancement of animal healthcare.

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Animal Behavior and Psychology:

• The impact of environmental enrichment on animal behavior and welfare
• Behavioral interventions for managing aggression in dogs
• Understanding the role of animal cognition in training and behavior modification
• The relationship between human-animal interaction and animal behavior
• Investigating stress and coping mechanisms in companion animals
• The effects of socialization on the behavior and development of puppies and kittens
• Exploring the psychological well-being of captive animals in zoos
• Behavioral indicators and management strategies for pain in animals
• Understanding the behavior and welfare of farm animals in intensive production systems
• Investigating the impact of fear and anxiety on animal welfare in veterinary settings

Infectious Diseases:

• Emerging zoonotic diseases and their impact on public health
• Antimicrobial resistance in veterinary medicine: challenges and strategies
• The role of vaccination in preventing infectious diseases in companion animals
• Epidemiology and control measures for common bacterial infections in livestock
• Investigating the transmission dynamics of vector-borne diseases in animals
• Diagnostic methods and advancements in the detection of viral infections in animals
• One Health approach: addressing the link between animal and human infectious diseases
• The impact of climate change on the prevalence and distribution of infectious diseases in wildlife
• Surveillance and control measures for emerging viral diseases in aquaculture
• Exploring the impact of biosecurity measures in preventing the spread of infectious diseases in veterinary clinics and hospitals

Pharmacology and Therapeutics:

• Investigating the efficacy and safety of new veterinary drugs and therapies
• Pharmacokinetics and pharmacodynamics of commonly used drugs in veterinary practice
• Adverse drug reactions and drug interactions in veterinary medicine
• Exploring alternative therapies in veterinary medicine: acupuncture, herbal medicine, and more
• The role of personalized medicine in veterinary practice
• Drug-resistant parasites and strategies for their control in companion animals
• Investigating the use of pain management protocols in veterinary surgery
• The impact of nutraceuticals and dietary supplements on animal health
• Pharmacogenomics in veterinary medicine: implications for personalized treatment
• Exploring the challenges and opportunities in veterinary drug development

Veterinary Surgery and Anesthesia:

• Advancements in minimally invasive surgery in veterinary medicine
• Anesthetic management and monitoring in exotic animal species
• Investigating surgical techniques for the treatment of orthopedic conditions in companion animals
• Complications and management of anesthesia in geriatric patients
• Exploring the role of regenerative medicine in veterinary surgery
• Surgical interventions for the management of oncological conditions in animals
• Investigating novel approaches for pain management in postoperative veterinary patients
• Surgical techniques and rehabilitation strategies for the treatment of spinal cord injuries in animals
• Exploring the use of robotic surgery in veterinary medicine
• Investigating the impact of surgical interventions on the quality of life in animals

Diagnostic Imaging and Radiology:

• Advancements in imaging techniques for the early detection of cancer in animals
• Investigating the use of magnetic resonance imaging (MRI) in veterinary neurology
• The role of ultrasound in diagnosing and managing cardiovascular diseases in animals
• Radiographic evaluation and interpretation of musculoskeletal disorders in small animals
• Investigating the use of computed tomography (CT) in veterinary oncology
• Diagnostic imaging in avian and exotic animal medicine
• The impact of advanced imaging modalities on the diagnosis of gastrointestinal diseases in animals
• Exploring the role of nuclear medicine in veterinary diagnostics
• Radiographic evaluation and interpretation of respiratory disorders in large animals
• Investigating the use of contrast-enhanced imaging techniques in veterinary medicine

Veterinary Public Health and Epidemiology:

• One Health approach in the surveillance and control of zoonotic diseases
• Investigating foodborne pathogens and their impact on animal and human health
• The role of veterinarians in disaster preparedness and response
• Veterinary epidemiology: studying disease patterns and risk factors in animal populations
• Investigating the impact of environmental factors on animal health and well-being
• Exploring the relationship between animal agriculture and antimicrobial resistance
• Veterinary public health interventions for the prevention of zoonotic diseases
• The role of wildlife in the transmission of infectious diseases to domestic animals
• Investigating the impact of climate change on vector-borne diseases in veterinary medicine
• Surveillance and control measures for emerging and re-emerging diseases in veterinary public health

Animal Nutrition and Feed Science:

• Investigating the impact of diet and nutrition on companion animal health
• The role of nutritional interventions in the management of obesity in animals
• Exploring the nutritional requirements and feed formulations for exotic animal species
• Nutritional strategies for the prevention and management of metabolic diseases in livestock
• Investigating the impact of feed additives on animal performance and health
• The role of probiotics and prebiotics in promoting gut health in animals
• Nutritional management of common gastrointestinal disorders in companion animals
• Exploring sustainable and environmentally friendly feed options for livestock
• Investigating the impact of nutrition on reproductive performance in animals
• Nutritional considerations for the optimal growth and development of neonatal animals

Veterinary Education and Professional Development:

• Evaluating the effectiveness of veterinary education programs in preparing students for practice
• Investigating the role of simulation-based training in veterinary education
• Exploring innovative teaching methods in veterinary schools
• Assessing the impact of continuing education on veterinary professionals’ knowledge and skills
• Investigating the factors influencing career choices among veterinary students
• The impact of telemedicine on veterinary practice and client communication
• Exploring the challenges and opportunities in veterinary entrepreneurship
• Veterinary leadership and management skills for effective practice management
• Investigating the role of mentorship in veterinary education and professional development
• Exploring the ethical considerations in veterinary practice and research

Equine Medicine and Surgery:

• Investigating advancements in diagnostic imaging techniques for equine lameness
• Management strategies for musculoskeletal disorders in performance horses
• The impact of nutrition and exercise on the prevention and management of metabolic diseases in horses
• Exploring the use of regenerative therapies in equine orthopedics
• Investigating the impact of respiratory diseases on the performance and welfare of horses
• Equine dentistry: advancements in dental care and oral health management
• Exploring novel surgical interventions for the treatment of orthopedic conditions in horses
• The role of physical therapy and rehabilitation in equine medicine
• Investigating the impact of exercise physiology on performance enhancement in horses
• Infectious diseases and vaccination strategies in equine healthcare

Wildlife Medicine and Conservation:

• Investigating the impact of habitat loss on wildlife health and conservation
• Wildlife forensic medicine: techniques for investigating wildlife crimes
• The role of veterinarians in wildlife rehabilitation and release programs
• Exploring the impact of emerging infectious diseases on wildlife populations
• Investigating the use of contraception in wildlife population management
• Wildlife anesthesia and immobilization techniques for veterinary interventions
• Exploring the role of veterinary medicine in endangered species conservation
• Investigating the impact of pollution and environmental contaminants on wildlife health
• Wildlife diseases and their potential for spillover to domestic animal populations
• Conservation genetics: utilizing molecular techniques in wildlife management

This comprehensive list of veterinary medicine research paper topics provides students with a wide range of subjects to explore within the field. Whether you are interested in animal behavior, infectious diseases, pharmacology, surgery, or any other aspect of veterinary medicine, there are countless opportunities for research and innovation. By selecting a topic that aligns with your interests and career goals, and following the expert advice on how to choose and write a research paper, you can contribute to the advancement of veterinary medicine and make a lasting impact on animal health and welfare.

Veterinary Medicine: Exploring the Range of Research Paper Topics

Veterinary medicine plays a vital role in the health and well-being of animals, from beloved pets to livestock and wildlife. As a student studying veterinary medicine, you have the opportunity to delve into various research areas and contribute to advancements in animal healthcare. This article will explore the diverse range of research paper topics available within the field of veterinary medicine, offering you insights into the exciting and impactful areas of study.

• Animal Nutrition and Feed Science : Proper nutrition is fundamental to the health and well-being of animals. Research topics in this area could include investigating the impact of diet and nutrition on companion animal health, exploring nutritional interventions for managing metabolic diseases in livestock, and examining sustainable and environmentally friendly feed options for animals.
• Infectious Diseases : Infectious diseases pose significant challenges to animal health and public health. Research paper topics in this category could encompass emerging zoonotic diseases and their impact on human health, antimicrobial resistance in veterinary medicine, vaccination strategies for preventing infectious diseases in animals, and exploring the transmission dynamics of vector-borne diseases.
• Animal Behavior and Psychology : Understanding animal behavior and psychology is essential for providing optimal care. Research topics in this field may involve studying the impact of environmental enrichment on animal behavior and welfare, behavioral interventions for managing aggression in dogs, investigating the cognitive abilities of animals, and exploring the role of human-animal interaction in animal behavior.
• Pharmacology and Therapeutics : Pharmacology plays a critical role in treating and preventing diseases in animals. Research paper topics in this area could include investigating the efficacy and safety of new veterinary drugs and therapies, exploring alternative therapies such as acupuncture and herbal medicine, and studying the pharmacokinetics and pharmacodynamics of commonly used drugs in veterinary practice.
• Veterinary Surgery and Anesthesia : Surgical interventions are often necessary for diagnosing and treating various conditions in animals. Research topics in this category could focus on advancements in minimally invasive surgery, investigating anesthesia management and monitoring in different animal species, exploring regenerative medicine in veterinary surgery, and studying the impact of surgical interventions on the quality of life in animals.
• Diagnostic Imaging and Radiology : Diagnostic imaging techniques play a crucial role in diagnosing and monitoring diseases in animals. Research paper topics in this field may include advancements in imaging techniques for detecting cancer in animals, exploring the use of magnetic resonance imaging (MRI) and computed tomography (CT) in veterinary diagnostics, and investigating the application of radiography and ultrasound in diagnosing specific conditions.
• Veterinary Public Health and Epidemiology : Veterinary medicine intersects with public health in various ways. Research topics in this area could involve the One Health approach in the surveillance and control of zoonotic diseases, studying the impact of environmental factors on animal and human health, and investigating the link between animal agriculture and antimicrobial resistance.
• Equine Medicine and Surgery : Horses require specialized veterinary care due to their unique physiology and performance demands. Research paper topics in this category may include investigating advancements in diagnostic imaging techniques for equine lameness, studying the management strategies for musculoskeletal disorders in performance horses, and exploring the impact of respiratory diseases on horse performance and welfare.
• Wildlife Medicine and Conservation : The health and conservation of wildlife are essential for maintaining biodiversity. Research topics in this field could include studying the impact of habitat loss on wildlife health, investigating wildlife rehabilitation and release programs, exploring the role of veterinarians in wildlife conservation, and understanding the diseases that affect wildlife populations.
• Veterinary Education and Professional Development : Ensuring the competency and continuous development of veterinary professionals is crucial. Research paper topics in this area may involve evaluating veterinary education programs, exploring innovative teaching methods, studying the impact of continuing education on veterinary professionals’ knowledge and skills, and investigating the factors influencing career choices among veterinary students.

The field of veterinary medicine offers a wide range of research opportunities, spanning various disciplines and species. Whether you are interested in animal nutrition, infectious diseases, surgery, diagnostic imaging, public health, or any other aspect of veterinary medicine, there are numerous fascinating topics to explore. By selecting a research paper topic that aligns with your interests and goals, you can contribute to the advancement of veterinary medicine, improve animal health and welfare, and make a meaningful impact in the field.

Choosing Veterinary Medicine Research Paper Topics

Selecting the right research paper topic is crucial for your success as a student of veterinary medicine. It allows you to delve into an area of interest, contribute to existing knowledge, and explore the latest advancements in the field. In this section, we will provide you with expert advice on how to choose veterinary medicine research paper topics that align with your interests and academic goals.

• Identify Your Interests : Start by reflecting on your personal interests within the field of veterinary medicine. Consider the areas that fascinate you the most, such as animal behavior, infectious diseases, surgery, diagnostic imaging, wildlife medicine, or public health. Identifying your passions will make the research process more enjoyable and rewarding.
• Consult Your Professors and Mentors : Seek guidance from your professors and mentors who have expertise in different veterinary medicine disciplines. They can provide valuable insights into current research trends, emerging topics, and areas that need further exploration. Discuss your interests with them, and they can help you narrow down potential research paper topics based on their knowledge and experience.
• Stay Updated with Current Literature : Stay abreast of the latest research publications, scientific journals, and conference proceedings in the field of veterinary medicine. Regularly reading scientific literature will expose you to new research findings, innovative techniques, and emerging topics. This will help you identify gaps in the existing knowledge that you can address through your research paper.
• Consider Relevance and Impact : When selecting a research topic, consider its relevance and potential impact on veterinary medicine. Look for topics that address current challenges, emerging issues, or areas where advancements are needed. Research that can contribute to animal health, welfare, conservation, or public health will not only be academically fulfilling but also have real-world implications.
• Analyze Feasibility : Assess the feasibility of your chosen research topic in terms of available resources, time constraints, and access to data. Consider the availability of research materials, laboratory facilities, animal populations, or specialized equipment required for your study. Ensure that your chosen topic is practical and achievable within the given timeframe and available resources.
• Collaborate with Peers : Consider collaborating with your peers or fellow researchers who share similar research interests. Collaborative research projects can broaden your perspective, enhance the quality of your research, and facilitate knowledge sharing. Engaging in interdisciplinary collaborations can also help you explore topics that combine veterinary medicine with other fields, such as biology, ecology, or public health.
• Seek Inspiration from Case Studies and Clinical Experience : Drawing inspiration from case studies, clinical experiences, or real-world scenarios can lead to intriguing research topics. Reflect on challenging cases you have encountered during clinical rotations, unique observations, or clinical questions that have piqued your interest. These experiences can spark ideas for research that address practical veterinary medicine issues.
• Consider Ethical Considerations : When choosing a research topic, consider ethical considerations related to animal welfare and human subjects. Ensure that your research adheres to ethical guidelines and regulations. If your research involves animal subjects, be mindful of the ethical treatment and use of animals, and obtain necessary approvals from relevant ethics committees.
• Explore Emerging Technologies and Techniques : Advancements in technology and techniques have a significant impact on veterinary medicine. Consider topics that explore the application of emerging technologies such as genomics, telemedicine, artificial intelligence, or novel diagnostic tools in veterinary practice. Research in these areas can contribute to the evolution of veterinary medicine and improve animal healthcare outcomes.
• Seek Practical Relevance and Application : Choose research topics that have practical relevance and application in the veterinary field. Look for topics that address challenges faced by veterinarians, animal owners, or the industry. Research that can provide evidence-based solutions, improve clinical practices, or enhance disease prevention and management will have a direct impact on veterinary medicine.

Selecting a suitable research paper topic is a crucial step in your journey as a veterinary medicine student. By identifying your interests, seeking guidance, staying updated with current literature, considering relevance and impact, and analyzing feasibility, you can choose a research topic that is both intellectually stimulating and practically valuable. Remember to consider ethical considerations, collaborate with peers, and explore emerging technologies. By following these expert tips, you will be well-equipped to embark on a research project that contributes to the advancement of veterinary medicine and makes a positive impact on animal health and welfare.

How to Write a Veterinary Medicine Research Paper

Writing a research paper in veterinary medicine allows you to contribute to the field, explore new knowledge, and develop critical thinking and scientific communication skills. In this section, we will guide you through the process of writing a veterinary medicine research paper, from selecting a topic to crafting a compelling paper that effectively communicates your findings.

• Define Your Research Objectives : Clearly define the objectives of your research paper. Determine what you aim to accomplish and the specific research questions you want to answer. This will provide a clear focus and direction for your study.
• Conduct a Thorough Literature Review : Begin by conducting a comprehensive literature review to gather existing knowledge and identify gaps in the research. Analyze and critically evaluate relevant studies, articles, and scientific literature to establish the context for your research.
• Refine Your Research Question : Based on your literature review, refine your research question or hypothesis. Ensure that your question is specific, measurable, achievable, relevant, and time-bound (SMART). This will guide your research and help you stay focused.
• Design Your Study : Select an appropriate research design and methodology that aligns with your research question and objectives. Determine the sample size, data collection methods, and statistical analyses required. Ensure that your study design is rigorous and ethically sound.
• Gather and Analyze Data : Collect relevant data using appropriate research methods, whether it involves conducting experiments, surveys, interviews, or analyzing existing datasets. Ensure that your data collection is thorough, reliable, and accurately recorded. Use appropriate statistical tools to analyze your data and draw meaningful conclusions.
• Organize Your Paper : Structure your research paper in a logical and organized manner. Include sections such as the introduction, literature review, methods, results, discussion, and conclusion. Follow a clear and coherent flow of information that guides the reader through your research process.
• Write an Engaging Introduction : Start your paper with an engaging introduction that provides background information on the topic, states the research problem, and highlights the significance of your study. Clearly articulate your research objectives and hypotheses to set the stage for the rest of the paper.
• Present a Comprehensive Literature Review : Incorporate a thorough literature review in the body of your paper. Summarize and critically analyze relevant studies, theories, and findings that inform your research. Identify gaps in the literature and highlight the unique contribution of your study.
• Describe Your Methods and Results : Clearly explain the methods you employed to conduct your research and gather data. Provide sufficient detail for others to replicate your study. Present your results objectively, using appropriate tables, graphs, or figures to support your findings. Interpret the results and discuss their implications.
• Engage in a Thoughtful Discussion : In the discussion section, interpret your findings in the context of existing knowledge and theories. Discuss the implications of your results, their limitations, and any future directions for research. Address any unanswered questions and propose areas for further investigation.
• Write a Strong Conclusion : Summarize your main findings and their significance in a concise and impactful conclusion. Restate your research objectives and hypotheses, and emphasize how your study contributes to the field of veterinary medicine. Avoid introducing new information in the conclusion.
• Cite Sources Accurately : Ensure that you cite all the sources used in your research paper accurately. Follow the appropriate citation style, such as APA, MLA, or Chicago, and adhere to the specific guidelines for referencing scientific literature and other relevant sources.
• Revise and Proofread : After completing the initial draft, revise your paper for clarity, coherence, and logical flow. Check for grammatical and spelling errors, and ensure that your writing is concise and precise. Seek feedback from peers, mentors, or professors to improve the quality of your paper.

Writing a veterinary medicine research paper requires careful planning, attention to detail, and effective communication skills. By defining your research objectives, conducting a thorough literature review, designing a rigorous study, and organizing your paper coherently, you can produce a high-quality research paper. Remember to write an engaging introduction, present a comprehensive literature review, describe your methods and results accurately, engage in thoughtful discussion, and provide a strong conclusion. Cite your sources properly and revise your paper meticulously. Through this process, you will contribute to the field of veterinary medicine and advance knowledge in the domain.

iResearchNet’s Writing Services

At iResearchNet, we understand the challenges that students face when it comes to writing research papers in veterinary medicine. We are here to provide you with professional writing services that cater to your specific needs. Our team of expert writers and researchers are well-versed in the field of veterinary medicine and can assist you in producing high-quality research papers. In this section, we will outline the range of services we offer and the benefits of choosing iResearchNet for your veterinary medicine research paper needs.

• Expert Degree-Holding Writers : Our team consists of expert writers with advanced degrees in veterinary medicine and related fields. They have a deep understanding of the subject matter and can deliver well-researched and meticulously written research papers.
• Custom Written Works : We provide custom written works that are tailored to your specific requirements. Whether you need a research paper from scratch or assistance with specific sections, our writers can create unique and original content that meets your academic standards.
• In-Depth Research : Our writers conduct extensive research to gather the most relevant and up-to-date information for your research paper. They have access to reputable sources and scientific databases to ensure the accuracy and validity of the information presented in your paper.
• Custom Formatting : We understand the importance of adhering to specific formatting styles required by academic institutions. Our writers are well-versed in various citation styles, including APA, MLA, Chicago/Turabian, and Harvard. They will format your paper according to the specific guidelines provided.
• Top Quality : Quality is our utmost priority. We strive to deliver research papers that meet the highest standards of academic excellence. Our writers pay attention to every detail, ensuring that your paper is well-structured, coherent, and free from grammatical errors.
• Customized Solutions : We recognize that each research paper is unique. Our writers work closely with you to understand your specific research objectives, requirements, and preferences. They can customize their approach to meet your specific needs and deliver a paper that aligns with your expectations.
• Flexible Pricing : We offer flexible pricing options to accommodate the budgetary constraints of students. Our pricing is competitive and transparent, ensuring that you receive the best value for your investment. We offer affordable rates without compromising on the quality of our services.
• Short Deadlines : We understand that students often face tight deadlines. Our team is equipped to handle urgent requests and can deliver high-quality research papers within short timeframes, even as tight as 3 hours. You can rely on us to meet your deadlines without compromising on quality.
• Timely Delivery : We prioritize timely delivery to ensure that you have sufficient time to review and submit your research paper. Our writers work diligently to complete your paper within the agreed-upon timeframe, allowing you ample time for any revisions or modifications you may require.
• 24/7 Support : We provide round-the-clock support to address any queries or concerns you may have. Our customer support team is available 24/7 to assist you with any questions regarding our services, order placement, or ongoing projects. Feel free to reach out to us at any time.
• Absolute Privacy : We understand the importance of confidentiality. Your personal information and the details of your research paper are treated with the utmost privacy and confidentiality. We adhere to strict data protection protocols to ensure the security of your information.
• Easy Order Tracking : Our user-friendly platform allows you to easily track the progress of your research paper. You can communicate with your assigned writer, provide additional instructions or clarifications, and monitor the status of your order throughout the writing process.
• Money Back Guarantee : We are committed to customer satisfaction. In the unlikely event that you are not satisfied with the quality of the research paper or the services provided, we offer a money-back guarantee. Your investment is protected, and we strive to ensure your complete satisfaction.

Choosing iResearchNet for your veterinary medicine research paper needs ensures that you receive professional, reliable, and customized writing services. Our team of expert writers, in-depth research capabilities, adherence to formatting guidelines, and commitment to quality will ensure that your research paper meets the highest academic standards. With flexible pricing options, timely delivery, 24/7 support, absolute privacy, and easy order tracking, we strive to make your experience with iResearchNet seamless and rewarding. Place your trust in us and let our expertise guide you towards academic success.

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Are you struggling with your veterinary medicine research papers? Do you find it challenging to choose the right topics, conduct in-depth research, and meet the high academic standards of your institution? Look no further! iResearchNet is here to provide you with the professional support you need to excel in your veterinary medicine studies. Our team of expert writers and researchers is ready to assist you in crafting top-quality research papers that will impress your professors and elevate your academic performance.

By choosing iResearchNet, you gain access to a range of benefits that will make your research paper writing experience smooth, efficient, and stress-free. Our team consists of highly qualified writers with expertise in veterinary medicine and related fields. They are equipped with the necessary knowledge and skills to tackle even the most complex research topics. Whether you need assistance in selecting research paper topics, conducting thorough research, or structuring your paper, our experts are here to guide you every step of the way.

iResearchNet is your trusted partner in veterinary medicine research paper writing. With our experienced team, commitment to quality, customer-oriented approach, and range of services, we are dedicated to helping you succeed in your academic journey. Place your trust in us and experience the difference that professional assistance can make. Take the first step towards veterinary medicine research paper excellence and unlock your full potential with iResearchNet.

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Research perspectives on animal health in the era of artificial intelligence

• Pauline Ezanno   ORCID: orcid.org/0000-0002-0034-8950 1 ,
• Sébastien Picault 1 ,
• Gaël Beaunée 1 ,
• Xavier Bailly 2 ,
• Facundo Muñoz 3 ,
• Raphaël Duboz 3 , 4 ,
• Hervé Monod 5 &
• Jean-François Guégan 3 , 6 , 7  

Veterinary Research volume  52 , Article number:  40 ( 2021 ) Cite this article

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Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.

1 Introduction

Artificial intelligence (AI) encompasses a large range of theories and technologies used to solve problems of high logical or algorithmic complexity. It crosses many disciplines, including mechanistic modelling, software engineering, data science, and statistics (Figure  1 ). Introduced in the 1950s, many AI methods have been developed or extended recently with the improvement of computer performance. Recent developments have been fuelled by the interfaces created between AI and other disciplines, such as bio-medicine, as well as massive data from different fields, particularly those associated with healthcare [ 1 , 2 ].

Interactions between animal health (AH), artificial intelligence (AI), and closely related research domains. This illustration is pinpointing only the links between AH (in blue), AI and its main subfields (in red), and other related fields of research (in black). It can be naturally complexified through the interactions between AH and other research topics (e.g., human medicine) or between core disciplines (e.g., statistics and physics).

AI addresses three challenges that also make sense in animal health (AH): (1) understanding a situation and its dynamics, e.g., epidemic spread; (2) the perception of the environment, which corresponds in AH to the detection of patterns (e.g., repeated sequence of observations), forms (e.g., of a protein) and signals (e.g., increased mortality compared to a baseline) at different scales; (3) computer-based decision making, or, more realistically, human decision support (e.g., expert systems, diagnostic support, resource allocation).

To answer these challenges, a wide range of concepts and methods are developed in AI. This includes machine learning (ML), a widely known AI method nowadays, which has been developing since the 1980s [ 3 ]. Since the 2000s, deep learning is developing with the rise of big data and the continuous increasing of computing capacities, enabling the exploration of massive amount of information that cannot be processed by conventional statistical methods. In addition, this also includes methods and algorithms for solving complex problems, automating tasks or reasoning, integrating information from heterogeneous sources, or decision support (Figure  1 ). These methods are now uprising in the human health sector, but are still rarely used to study animal health issues that they would help to revisit.

Part of the scientific challenges faced in AH can be approached from a new perspective by using some of these AI methods to analyse the ever-increasing data collected on animals, pathogens, and their environment. AH research benefits from advances in machine and deep learning methods, e.g., in predictive epidemiology, individual-based precision medicine, and to study host–pathogen interactions [ 2 , 4 ]. These methods contribute to disease diagnosis and individual case detection, to more reliable predictions and reduced errors, to speed-up decisions and improved accuracy in risk analysis, and to better targeting interventions in AH [ 5 ]. AH research also benefits from scientific advances in other domains of AI. Knowledge representation and modelling of reasoning [ 6 ] allow more realistic representations of complex socio-biological systems such as those encountered in AH. Examples include processes related to decision-making and uncertainty management [ 7 , 8 ], as well as of patient life courses like in human epidemiology [ 9 ]. This contributes to making them more readable by noncomputer experts. In addition, advances in problem solving under constrained resource allocation [ 10 ], in autonomous agents [ 11 ], multi-agent systems [ 12 ], and multi-level systems [ 13 ], as well as on automatic computer code generation [ 14 ] can be mobilised to enhance efficient and reliable epidemiological models. Interestingly, this may aid to anticipate the effect of control and management decisions at different spatial and temporal scales (animal, herd, country…).

Conducting research at the AH/AI interface also leads to identify new challenges for AI, on themes common with human health but considering different contexts and perspectives [ 15 ]. First, taking into account the particular agro- and socio-economic conditions of production systems is crucial when dealing with AH. Animal production systems depend on human activities and decisions. They can be a source of income (e.g., livestock) or labour forces and source of food in family farming. Citizens have also high expectations in terms of ethics and animal welfare [ 16 ]. Conventional measures to control animal diseases may no longer be acceptable by society (e.g., mass culling during outbreaks [ 17 ], antimicrobial usage, [ 18 ]). Alternatives must be identified and assessed, and AI can contribute. For example, individual-based veterinarian medicine is emerging, mobilising both AI methods and new AH data streams, these data differing from data in human health [ 19 ]. The integration of data from deep sequencing in AH, including emerging technologies for studying the metabolome and epigenome, is also a challenge [ 20 , 21 ]. Second, interactions between animal species, in particular between domestic animals and wildlife, lead to specific infectious disease risks (e.g., multi-host pathogens such as for African swine fever, pathogens crossing the species barrier facilitated by frequent contacts and promiscuity). The intensity of such interactions could increase due to separate or synergistic actions of environmental (e.g., landscape homogenisation, land use change for agriculture development, climate change), demographic (e.g., increasing global demand for animal production) and societal (e.g., outdoor livestock management) pressures. In addition, working on multi-species disease networks provides crucial information on the underlying molecular mechanisms favouring interspecific transmission [ 22 ]. Third, animal populations are governed by recurrent decision-making that also impacts health management (e.g., trade, control measures). Economic criteria as consequences on livestock farmers’ incomes are therefore essential indicators for evaluating AH control strategies, which can sometimes be misunderstood or may be at odds with societal expectations. These specificities make the AH/AI interface a theme of interest to stimulate new methodological work and to solve some of old and current locks faced by AH research today. With the development of new concepts in health such as One Health, Ecohealth and Planetary Health, promoting multidisciplinarity, stakeholders’ participation, data sharing, and tackling the complexity of health issues (e.g., multi-host pathogen transmission, short and long-term climatic impacts on disease patterns [ 23 ]), AI could participate in this new development by making it possible to technically solve some of the complex problems posed.

Mobilising the literature published at the AH/AI interface between 2009 and 2019 (Additional file 1 A), focusing our literature search on mainly livestock and wildlife, as well as interviews conducted with French researchers positioned at this interface (Additional file 1 B), we identified the main research areas in AH in which AI is currently involved country-wide. We explored how AI methods contribute to revisiting AH questions and may help remove methodological or conceptual barriers within the field. We also analysed how AH questions interrogate and stimulate new AI technical or scientific developments. In this paper, we first discuss issues related to data collection, organisation and access (Section  1 ), then we discuss how AI methods contribute to revisiting our understanding of animal epidemiological systems (Section  2 ), to improving case detection and diagnosis at different scales (Section  3 ), and to anticipating pathogen spread and control in a wide range of scenarios in order to improve AH management, facilitate decision-making and stimulate innovation (Section  4 ). Finally, we present the possible obstacles and levers to the development of AI in modern AH (Section  5 ), before making recommendations to best address the new challenges represented by this AH/IA interface (Section  6 ).

2 Collect, organise and make accessible quality data

A central point for research in AH remains the quality and availability of data, at the different organization levels of living systems and therefore at different spatial and temporal scales [ 24 ]. Data of interest are diverse. They can be obtained thanks to molecular analysis (e.g., genomic, metagenomics, or metabolic data), from observational data on individuals (e.g., body temperature, behaviour, milk production and composition, weight, feed intake), or from the production system (e.g., herd structure, breeding, management of sanitary issues). They can also be obtained at larger scales, beyond herds or local groups of animals (e.g., epidemiological data, demographic events, commercial movements, meteorological data, land-use occupation).

Even though the acquisition of these massive and heterogeneous data remain challenging (e.g., metabolome data), a large and diverse amount is already collected: (i) through mandatory reporting in accordance with regulations (e.g., commercial movements of cattle, epidemio-surveillance platform), (ii) by automatic devices (e.g., sensors, video surveillance systems), and (iii) on an ad hoc basis as part of research programs. This leads to a very wide diversity of data properties, and therefore of their management, access and possible uses. These data can be specifically obtained for certain animals or herds (e.g., during cohort monitoring programs) or by private companies (e.g., pig trade movements such as in France, milk collection). This can limit accessibility to academics and public research. Globalisation and large-scale animal trade may generate the need to use data obtained at worldwide scale in AH, especially for quantitative epidemiology (e.g., transcontinental spread of pathogens, animal genetics and breed management) leading to standardisation issues [ 25 ].

Consideration should be given to future systems for observing, collecting and managing these data [ 26 ], and to practices aimed at better collaboration between stakeholders. While data management has always been an important element in applied research to facilitate their use and valorisation, it is now a strategic issue both in theoretical and more applied research, coupled with a technical and algorithmic challenge [ 27 , 28 , 29 ]. Indeed, producing algorithms to manage massive data flows and stocks, by optimising calculations, is a challenge, particularly in real time. It seems also necessary to make heterogeneous data sources interoperable, requiring dedicated methodological developments [ 25 ]. In addition, much of the data is private, with ownership often heterogeneous (e.g., multiple owners, non-centralised data, closed data) and sometimes unclear (e.g., lack of knowledge of the real owner of the data between, for example, farmers, the data collector or the farmers’ union). All this tends to considerably complicate access to the data, raises questions about intellectual property, and raises questions in relation to regulations with regards to data protection, e.g., the adaptation of regulation to AH while respecting the confidentiality of the personal data mobilised. What is the relevant business model for data collection or access to existing databases? What about the openness of AH data (e.g., duality between the notion of public good and the private nature of certain data) to make it possible to experiment in real situations and compare the performance of AI algorithms? Answering these questions would facilitate the collection and sharing of ad hoc data. AI, particularly when combining a participatory framework with expert systems and multi-agent systems, helps to build realistic representations of complex socio-biological systems. Thus, it proves to be an effective tool to promote the collaboration of different stakeholders in collective and optimised decision-making, and to assess of the impact of changes in uses and practices [ 30 ].

Encouraging experimentation of AI technologies at a territorial scale becomes crucial to favour their development, validate their performance, and measure their predictive quality. In AH, simplified access to data-generating facilities would allow innovative solutions to be tested on a larger scale and would accelerate their development and evaluation. Substantial expertise exists (e.g., epidemiological data platform, large cohorts, experimental farms) that could be put to good use. In addition, AI could help to revisit sampling methods for field data collection in AH and epidemiological surveillance, by better and more dynamically targeting the data to be collected while avoiding redundant collinear, non-necessary data.

3 Contribution of AI to better understand animal epidemiological systems

Recent technological advances involving AI approaches have made it possible to obtain vast quantities of measurements and observations, as well as to store and share these data more efficiently. This has resulted in an increasing requirement for appropriate data analytical methods. AI methods emerged as the response of the computer-science community to these requirements, leveraging the exponential improvements in computational power. In parallel, statistical methods have greatly evolved in the last few decades as well, e.g., with regards to dimensionality-reduction in the spaces of variables and parameters, variable selection, and model comparison and combination. The rise in computational power has unleashed the development of Bayesian inference through simulation or approximate methods [ 31 ]. Bayesian methods have, in turn, facilitated the integration of data from diverse sources, the incorporation of prior knowledge and allowed for inference on more complex and realistic models while changing the paradigm of statistical inference [ 32 , 33 , 34 ].

3.1 Better understanding the evolution of AH and socio-ecological systems in a One Health context

Learning methods can be used to do phylogenetic reconstructions, contributing in particular to new evolutionary scenarios of pathogens and their transmission pathways. For example, phylogenetic models offer an interesting perspective for identifying environmental bacterial strains with high infectious potentiality [ 35 ], or for predicting the existence of putative host reservoirs or vectors [ 36 ]. The analysis of pathogen sharing among hosts has been used to classify the potential reservoirs of zoonotic diseases using machine learning [ 37 ]. The analysis of pathogen genomes can also be used to identify genotypes of animal pathogens that are more likely to infect humans [ 38 ].

Using phenomenological niche models that rely on data distribution more than on hypotheses about ecological processes at play, disease occurrence data or retrospective serological data coupled with environmental variables can be related to the risk of being exposed to a pathogen. Thus, they can help monitor potential spillovers and emerging risks and anticipate the epidemic pathogen spread [ 39 ]. For instance, Artificial Neural Networks (ANN) have identified the level of genetic introgression between wild and domesticated animal populations in a spatialized context [ 40 ], which may help to understand gene diffusion in host × pathogen systems involving multiple host species, and characterise specimen pools at higher risk to act as pathogen spreaders or sinks. Other AI approaches such as multi-agent models, a more mechanistic approach, have been used in an explicit spatial context for vector-borne pathogen transmissions, and proved to be sufficiently versatile to be adapted to several other particular contexts [ 12 ].

It should be noted here that several studies reveal the relatively ancient nature of AI research in AH. Such AI methods have often made it possible to identify signals (e.g., genetic introgression) or even particular patterns or properties (e.g., importance of density-dependence in the vector-borne transmission) that are less visible or hardly detectable by more conventional statistical treatments.

All these approaches contribute to better understand pathogen transmission in complex system networks as generally observed for emerging infections in tropical, developing regions of the world. On this matter, an improved knowledge is key for protecting humans against these new threats, and AI/AH interfaces development and training in cooperation with the poorest countries would facilitate synergistic effects and actions to predict and tackle new disease threats.

3.2 Reliability, reproducibility and flexibility of mechanistic models in AH

Better understanding and predicting pathogen spread often requires an explicit and integrative representation of the mechanisms involved in the dynamics of AH systems, irrespective of the scale (within-host: [ 41 ]; along a primary production chain: [ 42 ]; in a territory: [ 43 , 44 ]; over a continent: [ 45 ]).

Mathematical (equations) or computer-based (simulations) models can be used. Such mechanistic models (i.e., which represent the mechanisms involved in the infection dynamics), when sufficiently modular to represent contrasted situations, make it possible to anticipate the effects of conventional but also innovative control measures (e.g., new candidate molecules, sensors, genomic selection; [ 46 ]).

However, to assess realistic control measures, mechanistic epidemiological models require the integration of observational data and knowledge from biology, epidemiology, evolution, ecology, agronomy, sociology or economics. Their development can rapidly face challenges of reliability, transparency, reproducibility, and usage flexibility. Moreover, these models are often developed de novo, making little use of previous models from other systems. Finally, these models, even based on realistic biological hypotheses, may be considered negatively as black boxes by end users (health managers), because the underlying assumptions often became hidden in the code or equations.

The integration of multiple modelling perspectives (e.g., disciplines, points of view, spatio-temporal scales) is an important question in the modelling-simulation field. Epidemiological modelling could benefit from existing tools and methods developed in this field [ 47 , 48 , 49 ]. Although essential, good programming practices alone [ 50 ] cannot meet these challenges [ 51 ]. Scientific libraries and platforms accelerate the implementation of the complex models often needed in AH. For example, the R library SimInf [ 52 ] helps integrate observational data into mechanistic models. The BROADWICK framework [ 53 ] provides reusable software components for several scales and modelling paradigms, but still requires modellers to write large amounts of computer code.

New methods at the crossroads between software engineering and AI can enhance transparency and reproducibility in mechanistic modelling, fostering communication between software scientists, modellers and AH researchers throughout the modelling process (e.g., assumption formulation, assessment, and revision). Knowledge representation methods from symbolic AI, formalised using advanced software engineering methods such as domain-specific languages (DSL, e.g., in KENDRICK for differential equation models: [ 54 ]), makes model components accessible in a readable structured text file instead of computer code. Hence, scientists from various disciplines and field managers can be more involved in the model design and evaluation. Scenario exploration and model revision also no longer require rewriting the model code.

Other AI methods can improve model flexibility and modularity. Autonomous software agents enable to represent various levels of abstraction and organisation [ 55 ], helping modellers go more easily back and forth within small and larger scales, and ensure that all relevant mechanisms are adequately formalised at proper scales (i.e., scale-dependency of determinants and drivers in hierarchical living systems). Combining knowledge representation (through a DSL) and such a multi-level agent-based simulation architecture (e.g., in EMULSION, Figure  2 , [ 56 ]) enables to encompass several types of models (e.g., compartmental, individual-based) and scales (e.g., individual, population, territory), and it tackles simultaneously the recurring needs for transparency, reliability and flexibility in modelling contagious diseases. This approach should also facilitate in the future the production of support decision tools for veterinary and public health managers and stakeholders.

AI at the service of mechanistic epidemiological modelling (adapted from [ 51 ] ) . A . Modellers develop each epidemiological model de novo, producing specific codes not easily readable by scientists from other disciplines or by model end-users. B . Using AI approaches to combine a domain-specific language and an agent-based software architecture enhances reproducibility, transparency, and flexibility of epidemiological models. A simulation engine reads text files describing the system to automatically produce the model code. Complementary add-ons can be added if required. Models are easier to transfer to animal health managers as decision support tools.

3.3 Extracting knowledge from massive data in basic AH biology

Supervised, unsupervised and semi-supervised learning methods facilitate basic research development in biology and biomedicine, for example by using morphological analyses to study cell mobility [ 57 ]. The use of classification approaches and smart filters allows nowadays to sort massive molecular data (e.g., data from high throughput sequencing and metagenomics). Metabolic, physiological and immunological signalling pathways are explored, and metabolites are identified and quantified in complex biological mixtures, which was before a major challenge [ 58 ]. In addition, diagnostic time may be reduced by developing image analysis processing (e.g., accelerated detection of clinical patterns; [ 59 , 60 ]), often necessary to study host–pathogen interactions in animal pathology. For example, the use of optimisation methods has improved the understanding of the fragmentation of prion assemblages, contributing to a significant reduction in the time required to diagnose neurodegenerative animal diseases, thus paving the way for identifying potential therapeutic targets [ 61 ]. In livestock breeding, there is a methodological transition underway from traditional prediction strategies to more advanced machine learning approaches including artificial neural networks, deep learning and Bayesian networks which are being used to improve the reliability of genetic predictions and further the understanding of phenotypes biology. [ 62 ].

In human health, new disciplines have emerged in the second half of the 20 th century at the interface between AI and flagship disciplines, such as cell biology and immunology. Interface disciplines have developed, e.g., computational biology and immunology, which today must spread to AH. Current human immunology is based on the description of fine molecular and cellular mechanisms (e.g., the number of known interleukins has increased considerably compared to the 1970s). The desire to understand the processes underlying immune responses has led to a revolution by inviting this discipline to focus on complex systems biology and AI-based approaches [ 63 ]. However, the imbalance between the numbers of immunologists and immunology modellers is hampering the fantastic growth of this new discipline.

As an additional level of complexity, the hierarchical nature of biological systems makes that at the individual level, animals including humans must be considered as holobionts made of myriads of hosted microbial forms that form discrete ecological units (i.e., infracommunities). The potential of AI to grasp such diversity and complexity (e.g., tissue-specific microbiotes) and to scaling-up to higher levels of organization (e.g., component and compound communities of microbes, including pathogens, circulating in herd and in a given region) is certainly tremendous and should be studied with the same vigour as recent development in computational biology and immunology [ 40 ].

4 Revisiting AH case detection methods at different scales

Managing livestock health issues requires effective case detection methods, at the individual or even infra-individual (organ) scale, at the group/herd scale, or at larger scales (e.g., territories, countries). Machine learning methods allow detecting patterns and signals in massive data, e.g., in spatial data or time-series of health syndromes and disease cases, contributing to the development of smart agriculture and telemedicine (Figure  3 ). Alerts can be produced, and contribute to management advice in numerical agriculture [ 64 ] and veterinary practices [ 65 ]. AI may contribute to an earlier detection of infected cases and the rationalisation of treatments (including antimicrobials) in farm animals, by analysing data collected from connected sensors [ 66 ], by targeting individuals or groups of animals [ 59 ], or even by using mechanistic models to predict the occurrence of case detections and their treatment [ 67 ]. Also, machine learning methods enable to discriminate pathogen strains and thus to better understand their respective transmission pathways if different [ 68 ]. Finally, therapeutic strategies can be reasoned through multi-criteria optimisation, by identifying whom to treat in a herd, when, according to what protocol and for how long, in order to maximise the probability of cure while minimising both the risk of drug resistance and the volume or number of doses that are necessary (i.e., individual-based and precision medicine).

Extracting information from massive data to monitor animal health and better rationalise treatments.

Nevertheless, alert quality depends on the quality and representativeness of the datasets used by the learning algorithms. Numerous biases (e.g., hardware, software, human) can affect prediction accuracy. Moreover, alerts produced after training necessarily reflect the specificities of the system from which the data originates (e.g., area, period, rearing practices). Thus, result transposition to other epidemiological systems or to the same system subjected to environmental or regulatory changes remains risky. Furthermore, while machine learning methods (e.g., classification, image analysis, pattern recognition, data mining) provide solutions for a wide range of biomedical and bio-health research questions, it is crucial to demonstrate the performance of these methods by measuring their predictive quality and comparing them to alternative statistical methods whenever possible [ 69 ].

At the population level, case detection is based on direct (detection of syndromes) or indirect surveillance, mobilising syndrome proxies. Hence, the emergence of some animal diseases can be detected by syndromic surveillance, by detecting abnormal or rare signals in routine data (e.g., mortality, reproduction, abortion, behaviour, milk production, increased drug use; [ 70 ]). Also, serological data can be used retrospectively to identify individual characteristics related to a risk of being exposed to a pathogen, and thus orientate management efforts (e.g., in wildlife; [ 71 ]). Statistics and AI are largely complementary to address such issues. Both mobilise the wide range of available data, which are highly heterogeneous, massive and mostly sparse, to detect signals that are often weak or scarce [ 28 , 72 , 73 ]. Such signals can be proxy records (e.g., emergence of infectious diseases following environmental disturbances), health symptoms and syndromes, or even metabolic pathways in cascades which can be precursors of chronic or degenerative diseases. AI also includes methods to mobilise information available on the web. For example, semi-automatic data mining methods enabled to identify emerging signals for international surveillance of epizooties [ 74 ] or to analyse veterinary documents such as necropsy reports [ 75 , 76 ]. Methods from the field of natural language processing can compensate the scarcity of data by extracting syntactic and semantic information from textual records, triggering alerts on new emerging threats that could have been missed otherwise.

On a large to very large scale (i.e., territory, country, continent, global), data analysis of commercial animal movements between farms makes it possible to predict the associated epidemic risk [ 77 , 78 ]. These movements are difficult to predict, particularly since animal trade is based on many factors associated with human activities and decisions. Methods for recognising spatio-temporal patterns and methodological developments for the analysis of oriented and weighted dynamic relational graphs are required in this field because very few of the existing methods allow large-scale systems to be studied, whereas datasets are often very large (e.g., several tens or even hundreds of thousands of interacting operations).

On this topic, the specific frontier between learning methods of AI and statistics is relatively blurred, lying most on the relative prominence of the computational performance of algorithms versus mathematics, probability and rigorous statistical inference. While machine learning methods are more empirical, focused on improving their predictive performance, statistics is more concerned with the quantification and modelling of uncertainties and errors [ 79 , 80 ]. In the last decade, both communities have started to communicate and to mix together. Methods have cross-fertilised, giving birth to statistical models using synthetic variables generated by AI methods, or AI algorithms optimising statistical measures of likelihood or quality. New research areas, such as Probabilistic Machine Learning, have emerged at the interface between the two domains [ 1 , 80 , 81 ]. Meanwhile, machine learning and statistics have kept their specific interests and complementarity; machine learning methods are especially well-suited to processing non-standard data types (e.g., images, sounds), while statistics can draw inference and model processes for which only few data are available, or where the quantities of interest are extreme events.

5 Targeted interventions, model of human decisions, and support of AH decisions

5.1 choosing among alternatives.

A challenge for animal health managers is to identify the most relevant combinations of control measures according to local (e.g., farm characteristics, production objectives) and territorial (e.g., available resources, farm location, management priorities) specificities. They have to anticipate the effects of health, environmental and regulatory changes, and deliver quality health advice. The question also arises of how to promote innovation in AH, such as to anticipate the required characteristics of candidate molecules in vaccine strategies or drug delivery [ 82 , 83 ], or to assess the competitive advantage of new strategies (e.g., genomic selection of resistant animals, new vaccines) over more conventional ones. Private (e.g., farmers, farmers’ advisors) and collective managers (e.g., farmer groups, public authorities) need support decision tools to better target public incentives, identify investments to be favoured by farmers [ 46 ] and target the measures as effectively as possible: who to target (which farms, which animals)?; with which appropriate measure(s)?; when and for how long? These questions become essential to reasoning about input usage (e.g., antimicrobials, pesticides, biocides) within the framework of the agro-ecological transition.

The use of mechanistic modelling is a solution to assess, compare and prioritise ex ante a wide range of options (Figure  4 ; [ 84 ]). However, most of the available models do not explicitly integrate human decision-making, while control decisions are often made by farmers (e.g., unregulated diseases), with sometimes large-scale health and decision-making consequences (e.g., pathogen spread, dissemination of information and rumours, area of influence). Recent work aims to integrate humans and their decisions by mobilising optimal control and adaptive strategies from AI [ 7 , 85 ] or health economics methods [ 86 , 87 ]. A challenge is to propose clear and context-adapted control policies [ 88 ]. Such research is just starting in AH [ 46 ] and must be extended as part of the development of agro-ecology, facing current societal demand for product quality and respect for ecosystems and their biodiversity on one side, animal well-being and ethics on the other side, and more generally international health security.

Identifying relevant strategies to control bovine paratuberculosis at a regional scale (adapted from [ 76 ] ) . Classically, identifying relevant strategies means defining them a priori and comparing them, e.g., by modelling. Only a small number of alternatives can be considered. If all alternatives are considered as in the figure, it results in a multitude of scenarios whose analysis becomes challenging. Here, each point corresponds to the epidemiological situation after 9 years of pathogen spread over a network of 12 500 dairy cattle herds for a given strategy (asterisk: no control). Initially, 10% of the animals are infected on average in 30% of the herds. The blue dots correspond to the most favourable strategies. Mobilizing AI approaches in such a framework, especially optimization under constraints, would facilitate the identification of relevant strategies by exploring the space of possibilities in a more targeted manner.

5.2 Accounting for expectations and fears of animal health managers

Animal health managers should have access to model predictions in a time frame compatible with management needs, which is problematic in the face of unpredictable emerging events (e.g., new epidemiological systems, transmission pathways, trade patterns, control measures). Developing a library of models included in a common framework would strengthen the responsiveness of modellers in animal epidemiology. Relevant models would be developed more quickly and would gain accuracy from real-time modelling as epidemics progress [ 89 , 90 ]. However, if this makes move more quickly from concepts (knowledge and assumptions) to simulations and support decision tools, a gain in performance is still required to perform analyses at a very large scale. The automatic generation of high-performance computer code could be a relevant solution, which however remains a crucial methodological lock to be addressed in AI. In addition, it is often required to perform a very large number of calculations or to analyse very large datasets, which call for a rational use of computing resources. Software transferred to health managers sometimes require the use of private cloud resources (i.e., it does not run on simple individual computers), highlighting the trade-offs between simulation cost, service continuity (e.g., failure management) and time required to obtain simulation results [ 91 ]. These questions are currently related to computer science research, and collaborations are desirable between these researchers and those from AH.

Managers also wish to rely on accurate predictions from realistic representations of the biological systems. Before being used, model behaviour should be analysed, which raises the questions of exploring the space of uncertainties and data, and of optimization under constraints. This often requires intensive simulations, which would benefit from optimization algorithms to explore more efficiently the space of possibilities. In turn, this would allow, for example, the automatic identification of how to achieve a targeted objective (e.g., reducing the prevalence of a disease below an acceptable threshold) while being constrained in resource allocation. While this issue finds solutions in modern statistics for relatively simple systems, it represents a science front for complex systems (e.g., large scale, multi-host/multi-pathogen systems) that are becoming the norm. In addition, optimization goals specific to AH may generate ad hoc methodological needs [ 92 ]. The needs in abstraction and analysis capacity are massive and could benefit from complementarities between AI (e.g., reasoned exploration, intelligent use of computer resources, optimized calculations) and statistics to extract as much information as possible from the data: (1) explore, analyse, predict; (2) infer processes and emergent properties. Methodological developments are still required and would benefit many health issues, particularly in relation to the currently evolving concepts of reservoir-host, edge-host and species barrier [ 93 ]. Furthermore, methodological developments and dissemination of existing methods should be reinforced.

Finally, three barriers have been identified to the development of support decision tools for health managers, related to the societal issue of the acceptability of AI sensu lato, as a major factor of progress. First, ethical issues, which are obvious when it comes to human health, are just as important to consider in AH. Which AI-based tools do we want for modern animal husbandries and trades, and for which objectives? Are these tools not likely to lead to discrimination against farms according to their health status, even when this status cannot be managed by the farmer alone? Second, in AH too, there is a fear that AI-tools may replace human expertise. However, automating does not mean replacing human, his expertise and decision [ 94 ], but rather supporting his capacities for abstraction and analysis, accelerating the global process, making predictions more reliable, guiding complementary research. Nevertheless, a significant development of computer resources and equipment is not without impacting the environment in terms of carbon footprint (e.g., energy-intensive servers, recycling of sensors), which must also be accounted for. Third, the very high complexity of analysing results and acculturating end-users with knowledge issued from academic research, particularly AI, is an obstacle to the appropriation of AI-tools by their users. This may lead to the preference for simpler and more easily accessible methods. However, the latter may not always be the most relevant or reliable. Citizen science projects, also known as community participation in human epidemiology, enable AH to co-design and co-construct the AI-tools of tomorrow with their end-users [ 95 ], to better meet their expectations and needs, and to increase their confidence in the predictions of sometimes obscure research models, especially when they are hard to read (e.g., lines of code). Similarly, these AI-tools could be developed together with public decision-makers, livestock farmers, agro-food industries and sectoral trade unions. Co-construction gives time to explain the science behind the tools and makes it more transparent and useful. This citizen participation, which is nowadays supported in many countries, guarantees decisions more in line with citizens’ expectations and corresponds to a general trend towards structured decision-making. AI must contribute to this democratisation of aid in public decision-making in AH.

6 Barriers to the development of research at the AI/AH interface

Research conducted at the interface between AI and AH requires strong interactions between biological disciplines (e.g., infectiology, immunology, clinical sciences, genetics, ecology, evolution, epidemiology, animal and veterinary sciences) and more theoretical disciplines (e.g., modelling, statistics, computer science), sometimes together with sociology and economics. Conducting research at this interface requires strengthening the few teams already positioned in Western Europe, but also bringing together teams working around the concepts of One Health, Ecohealth and Planetary Health to benefit from recent achievements in infectious disease ecology and modelling, plant health and environmental health [ 96 ]. This work must be based on a wide range of methodological skills (e.g., learning methods, data mining, information systems, knowledge representation, multi-agent systems, problem solving, metamodeling, optimisation, simulation architecture, model reduction, decision models). The need for research, training and support are crucial issues at national, European and international levels. Also, a facilitated and trusted connection is required between academics, technical institutes, and private partners, who are often the holders or collectors of data of interest to solve AH research questions through AI approaches. The construction of better inter-sectoral communication and coordination must be done at supra-institutional level, as this theme seems hyper-competitive and as some current divisions still go against information and data sharing.

An acculturation of researchers to AI, its methods and potential developments, but also its limitations, must be proposed to meet the challenges of 21 st century agriculture. Indeed, there are obstacles to conducting research on this scientific front. Establishing the new collaborations required between teams conducting methodological work and teams in the fields of application remains difficult given the low number of academic staff on these issues, their very high current mobilisation and their low availability to collaborate on new subjects, as well as the difficulty of understanding and mastering these methods. There is a need for watching and training on AI methods available or under development, new softwares/packages, and their applicability. To develop key collaborations and establish a strategic positioning, an interconnection can also be made via transversal teams which appears as a preferential path. Solutions must also be found to encourage method percolation in the community and the development of scientific and engineering skills.

Finally, AI methods, such as classification, machine learning, data mining, and the innovations in AH to which these methods can lead are rarely discussed in veterinary high school education, whereas these students represent the future professionals of AH [ 96 ]. Similarly, there is a quasi-absence of sentinel networks of veterinarians, even if it is developing, although AH questions can arise on a large and collective scale. The scientific community would also benefit from further increasing its skills and experience in the valuation, transfer and protection of intellectual property on these AI methods and associated outcomes.

7 Levers to create a fruitful AI/AH interface

7.1 data sharing and protection.

No innovation at the interface between AI and AH is possible without strong support for the organisation of data storage, management, analysis, calculation, and restitution. The major risk is that demands for AI developments inflate without being supported by available human resources. In addition, an expertise in law, jurisdiction and ethics is required with regard to the acquisition, holding, use and protection of data in AH. This question must be considered at least at the inter-institutional/national level, and could benefit from a similar thinking already engaged in human health. The issue is to be able to support any change with regard to data traceability to their ownership, whether being from public or private domains.

New data are rich and must be valued as much as possible, not by each owner separately, but through data sharing and the mobilisation of multi-disciplinary skills to analyse such heterogeneous and complex data. Hence, data interoperability skills are required and must be developed. Models for making federated data sustainable over decades are required [ 97 ]. In addition, further encouraging the publication of data papers as valuable research products can help to develop the necessary culture of sharing, documentation and metadata.

Finally, to be able to launch ambitious experiments with AI methods on real data, it is necessary to (1) remove unauthorised access to data by negotiating with owners at large scale; (2) analyse and understand the related effect on methodological developments; and (3) if necessary, extend such initiatives to other areas, at national scale, or even across European countries.

7.2 Attract the necessary skills

An undeniable barrier to conduct such research comes from human resources, in particular the current insufficient capacity of supervision by permanent scientists. Collaborations are a solution to attract new skills. However, initiating collaborations at the AH/AI interface becomes very complicated because the qualified teams are already overwhelmed. Skill development at this interface must be supported, the cross-fertilisation of disciplines being essential. A watch on methods must also be carried out, accompanied by explanations for application fields, to train researchers and engineers. Financial incentives for scientist internships in specialised laboratories would increase skill capitalisation in advanced methods, while facilitating future national or international collaborations. In a context of limited resources as observed in many countries nowadays (e.g., new opened positions in national institutions) and limited experts pool (e.g., skills), facilitating post-doctoral fellows and continuing education of researchers becomes crucial. Finally, to consolidate the pool of future researchers in AH, promoting basic AI education in initial training of AH researchers, engineers and veterinarians is paramount.

More specifically concerning current research in immunology, cell biology and infectiology, the contribution of AI has been more widely considered in human health, which could feed a similar reflection in AH as locks and advances are not very specific. Before embarking on the fronts of science (e.g., emerging epigenomics and metabolomics in AH), a few persons from these biological disciplines should acculturate into AI, or even acquire autonomy in the use of methods [ 98 ], which internationally tends to be the trend [ 63 ]. This can be done through the sharing of experiences and basic training on existing methods, their advantages and limitations compared to other methods coming from statistics and mathematical modelling.

7.3 Encourage the development of AH/AI projects

Projects at the AH/AI interface, like any interdisciplinary project, must mobilise teams from both groups of disciplines and allow everyone to progress in their own discipline. However, identifying the issues shared between the most relevant disciplines requires a good acculturation of the disciplines between them, as well as an otherness aimed at better understanding each other [ 95 ], which is not yet the case at the AH/AI interface.

In terms of funding, European project calls offer interesting opportunities, but a significant imbalance persists between the ability to generate data and analyse complex issues, and the availability of human resources and skills to address such issues through AI methods or other modern methods in statistics, mathematics and computer science. The major international foundations (e.g., Bill and Melinda Gates) can also be mobilised on emerging infectious diseases at the animal/human interface (e.g., characterisation of weak signals, phenologies, emergence precursors), with a more significant methodological value. However, risk-taking is rarely allowed by funding agencies, although it is crucial to initiate interdisciplinary work. Dedicated incentive funding would support projects in their initial phase and make larger projects emerge after consolidation of the necessary disciplinary interactions.

Finally, these projects are generally based on the use of significant computing resources. Thus, research institutes and private partners should contribute in a financial or material way to the shared development of digital infrastructures, data centres, supercomputing centres on a national scale, as well as support recognised open-source software platforms on which a large part of the research conducted is based (e.g., Python, R ).

7.4 Promoting innovation and public–private partnership

Encouraging public–private partnership would promote a leverage effect on public funding and would make it possible to place AI research and development on a long-term basis in AH. Mapping the highly changing landscape of companies in the AH/AI sector, whether international structures or start-ups, would provide a better understanding of the possible interactions. Similarly, mapping academic deliverables produced at this interface would increase their visibility and highlight their potential for valorisation or transfer. Finally, considering the production of documented algorithms as scientific deliverables, along with publications, would help support this more operational research. More broadly, it would be advisable to initiate a communication and education/acculturation policy around AI and its development in AH (e.g., links with the society, farmers, agricultural unions, public services).

8 Conclusion

The use of AI methods (e.g., machine learning, expert systems, analytical technologies) converges today with the collecting of massive and complex data, and allows these fields to develop rapidly. However, it is essential not to perceive massive data and AI as the same trend, because the accumulation of data does not always lead to an improvement in knowledge. Nevertheless, the more data are numerous and representative of working concepts and hypotheses, the more important results can be obtained from AI applications. The underlying ethical, deontological and legal aspects of data ownership, storage, management, sharing and interoperability also require that a reflection be undertaken nationally and internationally in AH to better manage these data of multi-sectoral origin and their various uses. Moreover, while the effort to acquire such data is impressive, the development of AI skills within the AH community remains limited in relation to the needs. Opportunities for collaborations with AI teams are limited because these teams are already in high demand. To ensure that AH researchers are well aware of the opportunities offered by AI, but also of the limits and constraints of AI approaches, a training effort must be provided and generalized. Finally, the current boom in AI now makes it possible to integrate the knowledge and points of view of the many players in the field of animal health and welfare further upstream. However, this requires that AI and its actors accept to deal with the specificity and complexity of AH, which is not a simple library of knowledge that can be digitised to search for sequences or informative signals.

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Acknowledgements

This work has benefited from interactions with many French researchers (Additional file 1 B) interested in the AI/AH interface, for which we thank them here. We also thank Stéphane Abrioux, Didier Concordet and Human Rezaie for participating to the discussions.

PE is supported by the French Research Agency (project CADENCE: ANR-16-CE32-0007). XB is involved in the project “MOnitoring Outbreak events for Disease surveillance in a data science context” supported by the EU Framework Programme for Research and Innovation H2020 (H2020-SC1-BHC-2018–2019, Grant 874850). JFG is supported by both an “Investissement d’Avenir” managed by the French Research Agency (LABEX CEBA: ANR-10-LABX-25-01) and a US NSF-NIH Ecology of infectious diseases award (NSF#1911457), and is also supported by IRD, INRAE, and Université of Montpellier. The funding bodies had no role in the study design, data analysis and interpretation, and manuscript writing.

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Pauline Ezanno, Sébastien Picault & Gaël Beaunée

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Xavier Bailly

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Facundo Muñoz, Raphaël Duboz & Jean-François Guégan

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Hervé Monod

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PE carried out the literature review and analysed the interviews. PE and JFG conducted the interviews, drafted and wrote the manuscript. SP, GB, FM, RD, HM provided complementary views and references in their respective disciplines. All authors read and approved the final manuscript.

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Systematic literature review, interviews, previous publication in French.

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Ezanno, P., Picault, S., Beaunée, G. et al. Research perspectives on animal health in the era of artificial intelligence. Vet Res 52 , 40 (2021). https://doi.org/10.1186/s13567-021-00902-4

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• Animals used for fine needle aspiration cytology (FNAC)
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• Examine the veterinary nursing policies and practices of various nations and any potential working circumstances for nurses there
• An Investigation on how changing climate patterns affect the distribution of animal diseases and the practice of veterinary medicine.
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• Exploring recent advancements in surgical techniques for veterinary procedures
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The annual  Research Day  celebration at the College of Veterinary Medicine took place on April 17. Dr. Annette McCoy, associate professor of equine surgery and interim dean of research and advanced studies, coordinated the event. Dr. Miranda Vieson, clinical associate professor, helped serve as master of ceremonies.

Dr. McCoy noted that 85 abstracts describing exciting and diverse research had been submitted, setting an all-time high for this event. 

“The presenters include undergraduates, veterinary students, graduate students, faculty, staff, post-doctoral researchers, and research associates not only from our college but from across campus,” she said. 

This year the Pensinger Lecture in Comparative Medicine was given by  Dr. Kurt Hankenson , a Professor of Orthopaedic Surgery at the University of Michigan. His keynote presentation was entitled “Molecular and cellular determinants of bone regeneration: The long road to developing new treatments for bone healing.” He described not only his research journey in pursuit of ways to improve treatment options for people with broken bones but also his career trajectory, including roles at medical colleges and veterinary medical colleges.

Two graduate students were honored with awards. Vasiliki (Vicki) Mourikes was awarded the Joseph O. Alberts Award for excellence in research and scholarship. Shanny Hsuan Kuo  was awarded the Dr. Victor E. O. (Ted) and Carroll J. Valli Award for proficiency in research and diagnostic medicine.

Dr. Zachary Ready was awarded the Phi Zeta Literary Awards in Clinical Science. 

The Research Advisory Committee evaluated abstracts submitted by students and trainees who competed for the opportunity to win prizes in a speaking contest and in a poster contest. 

The six students selected for oral presentations were: 

• Mary Bunnell : Selective Deletion of ESR1 and ESR2 Lineage Cells Causes Infertility in Female Mice
• Jacob Maxon:   Prenatal Exposure to a Phthalate Mixture (PHT MIX) Causes Sex-specific and Dose-dependent Changes in Adult Social Behavior
• David Minich:  Dental Disease of Domestic Rabbits (Oryctolagus cuniculus): Treatments, Complications, and Outcomes (51 Cases)
• Matheus Moreno Passos Barbosa :  Anchored Immunotherapy with Canine Interleukin-12 (IL-12) is Safe and Induces Therapeutic Responses: Preliminary Results of an Exploratory Phase I Clinical Trial in Dogs with Advanced Melanoma
• Tara Prezioso:  A Network Evaluation of Human and Animal Movement Data Across Multiple Swine Farm Systems
• Jessica Wagner:  Single-dose Pharmacokinetics of Intranasal Levetiracetam in Healthy Dogs

Oral presentation winners were  Matheus Moreno Passos Barbosa , Jessica Wagner ,   and David Minich. Each received $1000 to be used toward research-related activities.

Six students were selected to present a “lightning talk” to attract visitors to their poster. The students and their topics were: 

• Rebecca Bishop:  Stability of Gastric Fluid and Fecal Microbial Populations in Pastured or Stabled Horses
• Samantha Hammack:  Serial Measurements of Circulating Myokine Levels in Exercised Foals: A Pilot Study
• Yishan Kuo:  Single-Dose, Intravenous and Oral Pharmacokinetics of Isavuconazole in Dogs
• Jinrui (Grace) Lyu:  The GABAA Receptor α5-selective Positive Allosteric Modulator, MP-III-022, Attenuates Postoperative Cognitive Impairments in Aged Mice Via Distinct Mechanisms
• Ji-Eun Oh:  Neonatal Exposure to Estrogen Impairs the Ovulation Machinery
• Zunaira Shoaib:  Bone Mechanics Influence Cancer Stem Cell Formation in Osteosarcoma

Lightning talk competition winners were  Zunaira Shoaib , Jinrui (Grace) Lyu , and Rebecca Bishop.  Each received $500 to be used for research-related activities. 

By Haley Maser

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The Veterinary Laboratory Investigation and Response Network (Vet-LIRN) is an FDA program created in 2010 to coordinate a network of veterinary diagnostic laboratories. In 2017, Vet-LIRN initiated an Antimicrobial Resistance (AMR) Monitoring Program focused on monitoring AMR profiles in veterinary and zoonotic pathogens isolated at veterinary clinics and diagnostic laboratories in U.S. and Canada. Vet-LIRN’s AMR program supports the national initiative to Combat Antibiotic Resistant Bacteria (CARB) and FDA’s Center for Veterinary Medicine multi-pronged, multi-year strategy designed to slow the emergence of resistance arising from the use of antibiotics in animals.

Vet-LIRN laboratories provided antimicrobial susceptibility testing (AST) data from clinically relevant bacterial isolates from animals: Salmonella enterica from any animal host, Escherichia coli and Staphylococcus pseudintermedius isolates from dogs, using a commercially available testing platform. Laboratories sequenced a subset of isolates and submitted the whole genome sequencing data (WGS) to the National Center for Biotechnology Information (NCBI) submission portal, where they were assembled and analyzed using NCBI’s bioinformatics pipelines. All sequencing data are publicly available. Laboratories follow Clinical and Laboratory Standards Institute AST testing methods. Vet-LIRN has partnered with the FDA’s National Antimicrobial Resistance Monitoring System (NARMS), to include animal pathogen AMR data into the NARMS integrated report and create publicly available data dashboards.

Since 2017, the Vet-LIRN AMR program has provided a wealth of information to detect emerging antimicrobial resistance, understand the effectiveness of FDA-regulated products over time, and foster antimicrobial stewardship in veterinary settings. For example, in 2019 we identified the carbapenem resistance gene, blaNDM-5, for the first time in the U. S., in a 2018 isolate of E. coli from a dog in Philadelphia. Over 15,000 isolates have undergone antimicrobial susceptibility testing, and more than 4,000 isolates have been sequenced with data publicly available under NCBI BioProject PRJNA314609. Vet-LIRN AMR data dashboards include resistance mechanisms from genomics data, along with the percent resistance and MIC distributions for each of the antibiotics included in the panels.

Conclusions:

By making AMR monitoring data publicly accessible, the Vet-LIRN AMR monitoring program facilitates international One Health research and surveillance.

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Potential new treatment path for lasting Lyme disease symptoms

Tulane University researchers have identified a promising new approach to treating persistent neurological symptoms associated with Lyme disease, offering hope to patients who suffer from long-term effects of the bacterial infection, even after antibiotic treatment. Their results were published in Frontiers in Immunology .

Lyme disease, caused by the bacterium Borrelia burgdorferi and transmitted through tick bites, can lead to a range of symptoms, including those affecting the central and peripheral nervous systems. While antibiotics can effectively clear the infection in most cases, a subset of patients continues to experience symptoms such as memory loss, fatigue, and pain -- a condition often referred to as post-treatment Lyme disease syndrome.

Principal investigator Geetha Parthasarathy, PhD, an assistant professor of microbiology and immunology at the Tulane National Primate Research Center, has discovered that fibroblast growth factor receptor inhibitors, a type of drug previously studied in the context of cancer, can significantly reduce inflammation and cell death in brain and nerve tissue samples infected with Borrelia burgdorferi. This discovery suggests that targeting FGFR pathways may offer an exciting new therapeutic approach to addressing persistent neuroinflammation in patients with post-treatment Lyme disease syndrome.

"Our findings open the door to new research approaches that can help us support patients suffering from the lasting effects of Lyme disease," Parthasarathy said. "By focusing on the underlying inflammation that contributes to these symptoms, we hope to develop treatments that can improve the quality of life for those affected by this debilitating condition."

Researchers treated nerve tissue with live or inactivated Borrelia burgdorferi, followed by an application of FGFR inhibitors. Study results revealed a significant reduction in both inflammatory markers and of cell death.

While further research is needed to translate these findings into clinical treatments, the study represents an important step forward in understanding and potentially managing the complex aftermath of Lyme disease.

This study was funded by the Bay Area Lyme Foundation and supported with resources from the Tulane National Primate Research Center base grant of the National Institutes of Health, P51 OD011104.

• Lyme Disease
• Diseases and Conditions
• Alzheimer's
• Veterinary Medicine
• Lyme disease
• Stem cell treatments
• West Nile virus
• Ovarian cancer
• Chemotherapy

Story Source:

Materials provided by Tulane University . Note: Content may be edited for style and length.

Journal Reference :

• Geetha Parthasarathy. Fibroblast growth factor receptor inhibitors mitigate the neuropathogenicity of Borrelia burgdorferi or its remnants ex vivo . Frontiers in Immunology , 2024; 15 DOI: 10.3389/fimmu.2024.1327416

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Strange & offbeat.

Veterinary care, animal hospitals are more scarce. That's bad for pets (and their owners)

Pets are part of our families.

That's how 97% of pet owners in the U.S. feel, according to Pew Research Center , which also found a majority of Americans, 62%, own at least one pet.

They're our companions and our friends, showing up at the door with wagging tails and snuggling in our lap with a deep purr, giving us unconditional love and making us smile just by being themselves. And like human family members, they need love, nourishment, socialization − and health care.

But as pet ownership numbers continue to skyrocket across the U.S., the number of vets has lagged well behind. Advocates fear there soon won't be enough veterinary doctors to give our fur babies vaccines, mend their injuries, treat their illnesses and even help us say goodbye when the time comes.

"We know there's a shortage, and it's worsening," said Barbara Hodges, a veterinarian who is director of advocacy and outreach for the Humane Society of the United States.

Black school's efforts: Historically Black college hopes to be second to train veterinarians. Why it matters.

What is causing a veterinary shortage?

According to a report by Mars Veterinary Health , which has a network of 3,000 veterinary clinics worldwide, the U.S. will need as many as 55,000 additional veterinarians by 2030 to meet the increasing need for companion animals' health care. This comes as spending on pets' health care is expected to increase 3% to 4% past the rate of inflation over the next decade or so.

Mars Veterinary Health estimates that by 2030, there may be a shortage of as many as 24,000 companion animal veterinarians, even accounting for students who will become vets over the next decade.

There are a few factors driving the shortage, said Hodges, a veterinarian since 2001.

More people own pets than ever before, fueled in part by the forced isolation of the pandemic and in part by the rise of remote and hybrid work. But the number of veterinarians is holding steady ‒ meaning companion care vets are seeing more animals and are busier than ever.

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"There are more constraints and pressures," Hodges said. "They have to work longer hours or see more patients; work-life balance is a big issue. So some veterinarians are leaving the profession, changing how they work or shifting to part-time work."

Becoming a veterinarian takes time: four years of undergraduate education and an additional four years of veterinary school. Eight years' college tuition means some graduates enter the workforce with a substantial student loan burden.

That can delay their ability to open new, independent practices. Some choose disciplines with a better work-life balance, such as research, product development, teaching or pharmaceuticals. And, much like human health care, veterinary medicine has become more specialized , so there aren't many general practice vets.

What is a 'veterinary desert'?

A study by the American Veterinary Medical Association identified so-called "veterinary deserts," using criteria including accessibility (such as transportation and distance), affordability (the pet owner's income level compared with the costs of care in their area) and availability (how many providers are in a given area).

"In a rural community, the closest veterinarian might be an hour away," said Amanda Arrington, vice president of access to care at the Humane Society of the United States. "And if you don't have transportation, accessing care can be nearly impossible."

People in urban areas might face different barriers to veterinary services, but they're no less daunting, she said.

Redlining and disinvestment have created pockets of poverty and the negative impacts of racial and economic segregation touch all aspects of peoples' lives, including their ability to access care for their pets.

Public transportation might be available for people but limited to service animals or smaller animals. And in densely populated areas, there might be longer waits for veterinary care at busy practices.

No matter where they live, elderly people or those with disabilities or mobility issues might find it difficult to travel to veterinary offices. People struggling with their own food or housing insecurity may have a hard time affording food for their pets, let alone veterinary care, grooming and other needs.

"There can be layers of barriers and challenges," Arrington said. "Rarely is someone living a one-challenge existence."

Advocates working to 'open doors' for new vets

Rena Carlson, president of the American Veterinary Medical Association, said there are a lot of students choosing to pursue veterinary medicine, a good sign given the expense and rigors of the discipline. New veterinary schools are being created, funded and accredited, as well, from Puerto Rico to Arkansas, Florida to Utah, South Carolina to Southern New Jersey .

"Nationwide, we're asking, how do we help open doors?" said Carlson, a veterinarian. "How do we guide (students) in making decisions about financing their education?" The group recognizes concerns about student debt, about providing opportunities for students from different backgrounds and about making sure graduates enter a veterinary discipline that's the best fit for them.

The AVMA is working to address those concerns and others through advocacy, scholarships and resources for vets and veterinary practices. It's also addressing burnout and increasing access to mental health care.

Pointing to a Merck-AVMA study , Carlson noted that 80% of vets and their staff believe what they do makes a positive difference in people's lives and their job satisfaction is generally high.

"They have a sense of purpose," she said.

Humane Society targets better access for pet owners

The Humane Society's Hodges said more people could access veterinary care through telemedicine. And she said vet technicians should be empowered to offer more standard care such as giving rabies vaccines and neutering male cats, freeing vets for more complex medical care.

The Humane Society offers scholarships for veterinary students and advocates for loan forgiveness programs for graduates who work for shelters, nonprofits or in underserved communities.

The Humane Society is also working to increase access in areas lacking it, Arrington and Hodges said. Its Pets for Life and Rural Area Veterinary Services initiatives seek to address inequities in access to care through outreach, advocacy, free and low-cost veterinary care , free pet supplies and other services. Pets for Life partners with organizations in 65 urban and rural communities and tribal nations across the country.

Vet bills soar: A big pet peeve: Rising costs of vet care bite into owners' budgets

"I hope more people will realize how important access to care is," Arrington said. "It's truly one of the defining issues of pet care. Pets mean so much to us; they are part of our families and everyone should be able to enjoy that love without being limited by geography or income."

Contact Phaedra Trethan by email at [email protected], on X (formerly Twitter) @wordsbyphaedra, or on Threads @by_phaedra .

• Frontiers in Veterinary Science
• Oncology in Veterinary Medicine
• Research Topics

Reviews in Veterinary Oncology

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Reviews in Veterinary Oncology will publish high-quality scholarly review papers on key topics. It aims to highlight recent advances in the field, whilst emphasizing important directions and new possibilities for future inquiries. We anticipate the research presented will promote discussion in the veterinary ...

Keywords : oncology, veterinary, animal cancer

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