The Future of AI-Facilitated Medicine | American Academy of Arts and Sciences

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An open access publication of the American Academy of Arts & Sciences

Winter/Spring 2026

The Future of AI-Facilitated Medicine

Author

Eric J. Topol

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To Dædalus issue

Abstract

AI’s most significant contribution to medicine to date is its ability to produce more accurate and comprehensive interpretations of medical images, as validated through randomized clinical trials. Next up is the opportunity to address the steady erosion of the patient-doctor relationship over several decades and a global burnout crisis among clinicians. By reducing the data clerical work of clinicians and giving patients more autonomy, AI has the potential to restore the humanity in medicine and care. Freeing physicians from constant screen time could bring back the physician’s presence during clinic visits and help foster empathy and trust. Furthermore, AI’s ability to aggregate and contextualize a “full stack” of patient data can enable new opportunities for prevention of major age-related diseases, significantly extending human health span. Despite this vast potential, the implementation of medical AI faces substantial challenges, including mitigating bias, ensuring data privacy and security, and establishing appropriate regulatory and reimbursement frameworks.

Author Information

Eric J. Topol is Chair of the Department of Translational Medicine at Scripps Research Institute, Director and Founder of the Scripps Research Translational Institute, Senior Consultant for the Division of Cardiovascular Diseases at the Scripps Clinic, and the Gary and Mary West Chair of Innovative Medicine in the Department of Translational Medicine at Scripps Research. He is the author of Super Agers: An Evidence-Based Approach to Longevity (2025), Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again (2019), and The Patient Will See You Now: The Future of Medicine is in Your Hands (2014).

The pioneers and leaders in the field of artificial intelligence have consistently singled out advances in medicine and health care as its most substantial and unequivocally positive impact. As Geoffrey Hinton put it: “I always pivot to medicine as an example of all the good it can do because almost everything it’s going to do there is going to be good.”1 Similarly, speaking about the end of disease, Demis Hassabis said: “I think that’s within reach. Maybe within the next decade or so, I don’t see why not.”2 Then there’s Dario Amodei, who believes AI will cure most cancers, prevent Alzheimer’s, and double the human lifespan, and Sam Altman, who predicted that “we will see diseases cured at an unprecedented rate thanks to AI.”3 And there’s Mustafa Suleyman, who said: “One of the applications that I’m most excited about is this quest for medical superintelligence. I think it’s pretty clear in the next couple of years we are going to build models that are better than the vast majority of expert clinicians in any discipline at diagnosis.”4<br>Encouraging as these proclamations are, the specifics of how AI can and will ultimately transform medicine remain vague. This essay intends to address the gaps and provide a futuristic perspective of how AI can achieve profound improvements in the practice of medicine and in health outcomes for patients—by improving diagnostic accuracy, restoring humanity to the doctor-patient relationship, and preventing diseases.<br>The medical community tends to minimize its errors. However, an important report by researchers at Johns Hopkins estimates that nearly eight hundred thousand Americans per year suffer permanent disability or death due to significant diagnostic medical errors.5 And that doesn’t include errors in treatment, which represent another serious problem. With the common use of the term “precision medicine,” there is no acknowledgment that if you make the same mistake repeatedly, it is, in a perverse way, very precise. We desperately need to promote diagnostic accuracy in medicine.<br>Today we’re making strides, particularly with respect to interpretation of medical imaging. The largest randomized trial in medical AI compared radiologists using AI against radiologists without AI in a sample of more than one hundred thousand women undergoing mammography.6 The radiologists working with AI support detected approximately 30 percent more instances of breast cancer and 25 percent more invasive tumors than radiologists without AI. Similarly, across forty randomized trials in gastroenterology, machine vision–supported colonoscopy (compared with colonoscopy without machine vision) increased the overall detection of adenomas and polyps by more than 20 percent.7<br>Beyond these randomized trials, there is supportive evidence for improved accuracy of all types of medical images, including X-rays, ultrasound, positron emission tomography (PET) scans, CT scans, and MRI. The next step is for the initial image review...