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\t\t\t\t I\u2019ve been a health care researcher for nearly 20 years, working in medical schools in the U.S., U.K., and now Sweden. My background is in the philosophy of science and mind, which might seem like a strange fit for discussing health care in the AI era. But for AI to fulfill its promise in medicine, expertise like mine can\u2019t remain peripheral. Domain experts in how humans and machines think are essential to optimizing patient care.<\/p>\n In chess, Garry Kasparov famously observed<\/a> that the strongest player is not the best human or the best machine, but a \u201cweak\u201d human paired with a strong machine and a better process. In this sense, \u201cweak\u201d doesn\u2019t mean incompetent \u2014 it means a human who knows when to let the machine lead.<\/p>\n Applied to medicine, the analogy sounds appealing: Keep the doctor, add the AI, get better results. But in practice, the \u201chuman\u201d is almost always assumed to be a traditionally trained doctor. We seldom ask whether a doctor is necessarily the right human for the role \u2014 or whether, and when, the role may not need a human at all.<\/p>\n This bias runs deep. In the 1950s, Paul Meehl showed that statistical models often outperform clinicians, yet decades later Berkeley Dietvorst found<\/a> people still abandon algorithms after minor errors. If we accept Kasparov\u2019s framing uncritically, we risk hard-coding these biases into future systems \u2014 preserving doctors\u2019 authority at the expense of patient outcomes.<\/p>\n Consider Matt Might, a computer scientist and rare disease advocate who trains undergraduates at the University of Alabama to use an AI tool called MediKanren. The system scans vast biomedical datasets, spotting hidden links between symptoms, diseases, and potential treatments.<\/p>\n In six years, he told me, they\u2019ve worked with 600 patients, and in nearly half of those cases their suggestions pointed to actionable treatment options that doctors could meaningfully pursue. That success comes from process: running targeted queries, curating results, and delivering them in a form that physicians can use.<\/p>\n This is Kasparov\u2019s formula in action: a \u201cweak\u201d human paired with a capable machine and, crucially, a strong process. The students aren\u2019t doctors, yet their structured collaboration with AI apparently improves care. If undergraduates can deliver value like this, why assume doctors must sit at the center of every reasoning task? The real question is: Who \u2014 or what \u2014 gets the best result for the patient?<\/p>\n Part of the problem is that we design health care around the assumption that human judgment \u2014 specifically doctors\u2019 judgment \u2014 is the gold standard. But patients don\u2019t seek care for a doctor\u2019s opinion as an end in itself; they seek health.<\/p>\n \t\t\t \t\t\t\t As Oxford AI scholars Richard and Daniel Susskind ask: What is the question to which human judgment is the answer? Framing it this way forces us to design systems around outcomes, not egos. The goal isn\u2019t to preserve a role \u2014 it\u2019s to get the best result for the patient, whether that comes from a human, a machine, or a process neither could execute alone.<\/p>\n For nearly a decade, my surveys have found physicians view AI mainly as a way to shed paperwork and \u201cget back to doctoring\u201d \u2014 assuming doctoring is fixed. But cognitive science shows humans hit a ceiling even in optimal systems. AI raises that ceiling, so the very architecture of the profession must change.<\/p>\n Many leading doctors, even celebrated health informatics visionaries, call medicine an \u201cinformation processing field,\u201d yet rarely explain what that actually entails.<\/p>\n In \u201cDr. Bot,\u201d I unpack this claim and argue that such questions lie squarely in the domain of cognitive science and related fields that study how humans and machines think. Yet we are still building health care on analog-era assumptions in a digital century. AI literacy is minimal. Cognitive science barely features.<\/p>\n Moreover, if AI is going to reshape the cognitive division of labor in health care, it\u2019s not just education that needs reform \u2014 it\u2019s the entire set of systems and workflows that determine how humans and machines work together. Failing to redesign those is not just shortsighted; it\u2019s structurally unsound.<\/p>\n We need our brightest interdisciplinary minds \u2014 philosophers, cognitive scientists, ethicists, data scientists \u2014 working alongside clinicians to decide which tasks belong to humans, which to machines, and which to redesigned processes neither could perform alone. Sometimes leadership will shift to colleagues who aren\u2019t doctors at all.<\/p>\n AI in medicine isn\u2019t about bolting automation onto old workflows. It\u2019s about deciding, with moral and cognitive clarity, what the medical profession is for. That may redistribute authority, status, and even the definition of \u201cdoctor.\u201d If we can face that, we can design a profession \u2014 or, more accurately, professions \u2014 that match the realities of 21st-century cognition, technology, and patient needs.<\/p>\n If we can\u2019t, we\u2019ll keep mistaking motion for progress. And patients like Liam will keep finding their answers elsewhere.<\/p>\n Charlotte\u202fBlease, Ph.D., is associate professor at the Department of Women\u2019s and Children\u2019s Health, Uppsala University and research affiliate at digital psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center. Her book, \u201cDr.\u202fBot: Why Doctors Can Fail Us and How AI Could Save Lives<\/a>,\u201d is out now from Yale University Press.<\/p>\n","protected":false},"excerpt":{"rendered":"\u201cMy watch saved my life.\u201d Liam \u2014 not his real name \u2014 is a 75-year-old retired teacher in…\n","protected":false},"author":2,"featured_media":132242,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34],"tags":[276,49,48,84,392,2099],"class_list":["post-132241","post","type-post","status-publish","format-standard","has-post-thumbnail","category-healthcare","tag-artificial-intelligence","tag-ca","tag-canada","tag-health","tag-healthcare","tag-physicians"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/132241","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/comments?post=132241"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/132241\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media\/132242"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media?parent=132241"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/categories?post=132241"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/tags?post=132241"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"\/](\"https:\/\/www.statnews.com\/wp-content\/themes\/stat\/images\/home\/statplus.svg\")
\n\t\t\t\tWhy AI like Grok isn\u2019t ready for the radiology big leagues<\/a><\/p>\n![\"\"](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2025\/09\/AdobeStock_238730671-768x432.jpeg\")
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\n\t\t\t\tMedicine\u2019s AI era urgently demands new doctor-patient relationship<\/a><\/p>\n