Annals commentary: 'human-in-the-loop' AI oversight breaks down under clinician workload
EricTopol · x · 2026-10-30
A commentary in Annals of Internal Medicine (amplified by Eric Topol) argues that the human-in-the-loop model underpinning most clinical AI may be far less effective than intended when clinicians face high workloads and limited time to review outputs. The piece maps representative deployments — diagnostic decision support, ambient documentation, sepsis early warning, endoscopy CAD, autonomous prescription renewal — to specific oversight failure modes: automation bias, alert fatigue, fluent AI-drafted errors that go undetected once signed, skill erosion, and sampling that can't catch rare or adversarial failures. The authors call for stronger oversight, testing, and continuous monitoring as AI's role in healthcare expands.
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