AI systems, like Google's system for reading mammograms, have the potential to worsen pre-existing problems like overtesting, overdiagnosis, and overtreatment
A new study out from Google seems to show the promise of AI-assisted health care. Actually, it shows the threat. Tweets: @robmay , @allenfrancesmd , @went1955 , @edwardtufte , @wired , @nathancortez , @cragcrest , and @vprasadmdmph Tweets: Rob May / @robmay : Yes. AI can be wrong. What will doctor liability be when they go against the advice of the AI? https://www.wired.com/... Allen Frances / @allenfrancesmd : Great piece on the big risk Artificial Intelligence will amplify the mistakes of the bad medicine often practiced today. If you start with unintelligent assumptions, AI will cause even more over-diagnosis/harmful treatment than we already have. All hi-tech glitter is not gold. https://twitter.com/... Robert Went / @went1955 : AI Makes Bad Medicine Even Worse. A new study out from Google seems to show the promise of AI-assisted health care. Actually, it shows the threat. Mark Zuckerberg's motto, “Move fast and break things” is not great for medicine, AI-assisted or not https://www.wired.com/... Edward Tufte / @edwardtufte : A+ report on chronic long-term AI diagnostic issues: garbage in, garbage out cascades of false alarms bad medicine made worse In contrast, NYT gullible account “Using A.I. to Transform Breast Cancer Care” says AI might PREDICT TREAT CURE, and even PREVENT cancer https://twitter.com/... @wired : Machine-enabled health care may bring us many benefits in the years to come, but those will be contingent on the ways in which it's used. If doctors ask the wrong questions to begin with, the technology could serve to amplify our earlier mistakes. https://www.wired.com/... Nathan Cortez / @nathancortez : There's a ton of hype with A.I. in medicine and skeptics are often called Luddites. But this article (in @WIRED!) does a great job explaining why A.I. isn't a panacea and even creates its own risks. https://www.wired.com/... Christie Aschwanden / @cragcrest : That new Google study on artificial intelligence for breast cancer screening isn't an example of how AI will revolutionize medicine. It's a cautionary tale of how AI could make bad medicine worse. https://www.wired.com/... Vinay Prasad / @vprasadmdmph : Both for content and writing, this is simply the perfect essay. https://twitter.com/...
Context & Ripple Effects
Google's mammogram-reading model was pitched as proof that AI can transform breast cancer care, but the critique in this piece argues the opposite: a system tuned to find more cancers feeds directly into overtesting, overdiagnosis, and overtreatment — pathologies Allen Frances and other critics say already define much of modern medicine. The concern landed just as hospitals began adopting such tools: within months, STAT reported clinics rolling out novel, largely unproven AI decision-support systems with most patients unaware they were in use.
The arc since has tracked the warning. By 2023, the Wall Street Journal found US hospitals running flawed diagnostic AI while some clinicians felt pressure from administrators to defer to the algorithm — exactly the liability question Rob May raised about doctors who override an AI's advice. And by 2025, Wired was documenting patients and physicians alike acting on confident-sounding answers that lack clinical nuance, suggesting the overdiagnosis critique generalized well beyond mammography.
First-order effects
- Hospitals deploying AI decision support face immediate pressure to validate tools before clinicians rely on them — the WSJ reporting shows some clinicians already feel pushed to follow algorithmic output over their own judgment.
Second-order effects
- Vendors of diagnostic AI must now compete on demonstrated impact on patient outcomes rather than detection sensitivity, since a tool that flags more findings shifts costs onto downstream biopsies, treatment, and liability.
Third-order effects
- If the pattern holds, regulators and hospital governance bodies will need to evaluate AI not just for accuracy but for whether it amplifies existing practice pathologies — a shift from 'does it work' to 'does it make medicine worse' as the approval bar.
The trend: Medical AI is moving from promise-driven pilots toward accountability for amplifying medicine's existing failures, with deployment outpacing evidence at every step.