To Do No Harm — and the Most Good — with AI in Health Care

Carey Beth Goldberg, Laura Adams, R.N., M.S., David Blumenthal, M.D., M.P.P. , Patricia Flatley Brennan, R.N., Ph.D., Noah Brown , Atul J. Butte, M.D., Ph.D., Morgan Cheatham, Dave deBronkart, Jennifer Dixon, M.D., Ph.D., Jeff Drazen, M.D., Barbara J. Evans, Ph.D., J.D., L.L.M., Sara M. Hoffman, Chris Holmes, Ph.D., Peter Lee, Ph.D., Arjun Kumar Manrai, Ph.D., Gilbert S. Omenn, M.D., Ph.D. , Jonathan B. Perlin, M.D., Ph.D. , Rachel Ramoni, D.M.D., Sc.D., Guillermo Sapiro, M.S., Ph.D., Rupa Sarkar, Ph.D. , Harpreet Sood, M.B.B.S., M.P.H., M.R.C.G.P., F.F.C.I., Effy Vayena, Ph.D., and Isaac S. Kohane, M.D., Ph.D.

Recently the RAISE (Responsible AI for Social and Ethical Healthcare) conference, which was organized by the Department of Biomedical Informatics at Harvard Medical School was held October 29–31, 2023, in Cape Neddick, Maine. The conference agenda and attendees can be found here.

The RAISE conference did not aim to reach a comprehensive consensus on all aspects of AIH. Instead they focused on a few key aspects and followed this with a simultaneous publication in two journals NEJM AI and Nature Medicine with the goal to stimulate an open discussion on these topics that can inform policymakers, companies, and professional societies in their own construction of policy and practice.

The article discusses the ethical and practical considerations for implementing Artificial Intelligence in Health Care (AIH). While this focuses on human health, the proposed ideas can be extrapolated to veterinary medicine.

• Ethical Imperative: AIH must prioritize patient benefit, addressing health care system flaws like medical errors and access disparities. In veterinary medicine, this translates to focusing AI applications on improving animal health outcomes and supporting veterinary professionals. 

• Adoption Urgency: With modern medicine’s complexity, AIH is essential for tasks beyond human capacity. For veterinary medicine, adopting AI can enhance precision treatments for animals, mirroring the human health care advancements. 

• Inclusivity and Evaluation: AIH should be accessible and continuously evaluated for safety and effectiveness. Similarly, veterinary AI tools must be designed to be inclusive of various animal species and conditions, ensuring their safe and effective use across veterinary practices. 

• Financial Models: AIH requires transparent financial models aligned with care improvement. Veterinary medicine should adopt similar models, ensuring AI tools are cost-effective and add value to veterinary services. 

The paper puts forth a series of calls to action including the following four for health system leaders:

A. Adoption of AI in Clinical Practice

• Health systems, plans, and physician groups should consider adopting AI.

• AI can enhance doctor-patient interaction, analyze test results, assist in diagnosis and therapy planning, and handle administrative tasks.

B. Transparent Financial Models

• Reimbursement models for AI use should be transparent and evaluated annually.

• Establishing fair payment paradigms is crucial.

C. Guidelines for AI Use

• Institutions should establish guides for optimal AI use for clinicians, trainees, and patients.

• Patients and stakeholders should advocate for widespread AIH education and modeling.

D. Outcome Expectations

• Clear outcome expectations should be created to ensure AI serves patients’ and providers’ interests.

• Publicly defined metrics should be rigorously assessed and shared across health systems.

These calls to actions apply similarly to veterinary health systems.

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