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Every article we have published on Clinical Decision Support, newest first.
Linking exome data to electronic medical records solved 15% of unexplained hearing-loss cases and flagged new candidate genes — while showing what the records could not supply.
A Danish stepped-wedge trial gave endoscopists automated feedback on their technique after every procedure. Adenoma detection rose from 43.4% to 48.6% — a different tool from real-time polyp AI.
Applied to 411,000 UK Biobank adults, the Edmonton staging system and the Lancet Commission's clinical-obesity model classified severe disease alike but diverged on who counts as early-stage.
In 6,772 users of a US health system's AI triage tool, people engaged about twice as often when the AI matched what they already planned — raising a hard question about what the tools are steering.
A review of 41 studies found that the great majority of AI medication-adherence prediction models carried high risk of bias, and that fancier algorithms did not reliably predict better.
Relationality, self-governance, competence. The position paper's starting premise is that consensus on privacy, disclosure and fairness has not been reached, and clinicians need guidance anyway.
A network meta-analysis of 53 studies and more than 7 million admissions finds machine learning out-discriminates traditional sepsis scores — and would raise roughly two false alarms for every real one.
The tool reads vital signs, lab results, and history the way clinicians already do — just faster and continuously. Its accuracy tops out around 90%, which means it's also still wrong a meaningful share of the time.
START-AI reads triage comments, case notes and whether anyone ordered a blood test. Adding heart rate and blood pressure to the model produced no measurable improvement — a result the team published rather than buried.
A Lancet Digital Health scoping review found the field's fairness metrics fragmented and rarely clinically validated. A second review found that most studies don't measure fairness at all.
Maccabi Healthcare Services studied 626 of its own physicians to work out who follows algorithmic prescribing advice — and found the pattern was about practice structure, not just attitude.
A small proof-of-concept study pitted several AI systems against gastroenterologists and emergency physicians on cholangitis exam questions. The gap between the best and worst AI performers was enormous.
After 12 months, HbA1c was 6.5% among people managed by community health workers versus 7.1% among those referred to clinics. With 103 participants analysed, the confidence interval crosses zero.
DxDirector-7B beat human physicians on a benchmark of complex diagnostic cases while requesting far fewer tests. Its own authors say it isn't ready for high-risk or emergency cases.
Ten studies, wide confidence intervals, and factual error rates of 26 to 36 percent in AI-drafted documentation. The review's own conclusion is that the evidence is preliminary and highly uncertain.
Revised guidance reverses the 2022 position that a single recommendation made software a device. The agency's own criterion — that a clinician can independently review the basis — now carries the weight.
Ten sonographers estimating gestational age got sharply more accurate when shown model predictions. Adding visual explanations improved the average further but made several individuals worse.
Singapore General Hospital randomised residents to work with and without an LLM assistant. Documentation time fell by 1.82 minutes, which was not significant. The economic model used the point estimates anyway.