MedicsTop 10 AI in Health Care
The week’s ten biggest AI-in-medicine papers and stories, with commentary from two doctors who have seen a few hype cycles.
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Trump admin using AI to deny medical care for seniors in disastrous experiment
Our take: Prior authorization has reached its final form: a denial engine that never sleeps, never eats and never has to open the chart. Appeals will presumably be processed with equal enthusiasm.
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Insurers claim AI is already increasing healthcare costs
Our take: Insurers, pioneers of the algorithmic claim denial, are alarmed that hospitals now have algorithms too. The arms race has reached parity, and the patient is standing in the middle of the field.
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AI will inflate healthcare costs before lowering them, Oz says
Our take: The most honest AI pitch deck yet: costs go up now, savings arrive later. Medicine has heard this one before, about every EHR rollout since 2009, and is still waiting on "later."
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Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence
Our take: A genuinely clever idea: look for esophageal cancer on the CT scans people were already getting for something else. The incidentaloma clinic should start ordering more chairs.
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An OpenAI Agent Hacked Australia’s Health Service. Their Government Found Out Months Later
Our take: An AI agent got into a national health service and nobody noticed for months, which is still faster than most hospitals notice anything. The fax machine remains undefeated on cybersecurity.
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Preparing AI chatbots to respond to patient distress and suicidality in high-risk healthcare settings
Our take: The serious question of the week: what should a chatbot do when someone in crisis messages it at 2 a.m.? The right answer involves a human being and the 988 Suicide & Crisis Lifeline, not a cheerful disclaimer.
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Comparative evaluation of ambient digital scribe systems in clinical documentation
Our take: Someone finally compared ambient AI scribes head to head, answering the question every clinic asked roughly one week after signing the three-year contract.
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The rise of the AI-native radiology practice
Our take: The AI-native radiology practice has arrived. Radiologists, promised for a decade that they would be replaced, are now being "augmented," which is the word used right before the staffing model changes.
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Overseeing Agentic AI in Medicine From First Principles
Our take: Before AI agents start ordering tests on their own, someone wants to work out from first principles who supervises them. Medicine's traditional answer is the intern, at 3 a.m., untrained.
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Epic’s mortality model, and Omada’s future products
Our take: Epic has a mortality model, which is either a triumph of predictive analytics or the most ominous pop-up in the chart. Clinicians will learn to click past it along with the other 400 alerts.
Headlines link to the original papers and stories; the commentary under each is ours, and it is satire. We quote no article text and aim the jokes at the hype, never at the authors or reporters.
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