Version 2 of this summary, as approved on . It weighed 280 gains, 85 problems and 93 Traces. This is the current version. See the topic as it stands today
Most of the evidence here is retrospective accuracy testing on past scans and records, with few randomized trials and little tracking of what happens to patients afterwards. TRV-2026-1434TRV-2026-1490TRV-2026-0486 Reviewers repeatedly say strong test scores have not yet been matched by proof of better care. TRV-2026-1017TRV-2026-0486TRV-2026-1924 Results look best on narrow detection tasks and weakest when models move to new hospitals or populations. TRV-2026-0638TRV-2026-1244TRV-2026-1220
A randomized trial of 14,665 adults showed AI-ECG-guided monitoring found new atrial fibrillation, flutter or pre-AF findings in 0.74% of intervention participants versus 0.48% of controls, though the authors call the result statistically fragile. TRV-2026-2462 In a randomized trial of 300 people with suspected inherited retinal disease, eye specialists using an AI tool had the right gene category among their top four answers 81.8% of the time versus 53.1% alone. TRV-2026-0575 A systematic review found AI-read ECGs detected weak heart pumping in people without symptoms with pooled AUROC of 0.87 to 0.93, and another review found AI detected STEMI heart attacks with sensitivity 0.91 versus 0.78 for clinicians. TRV-2026-1837TRV-2026-2312 A systematic review of pooled studies found AI assistance raised doctors' fracture detection sensitivity to 87% from 73%, with junior clinicians gaining most. TRV-2026-1145
A randomized trial of 171 veterans found AI-guided monitoring after a heart failure hospitalization did not significantly reduce rehospitalization (16.3% versus 18.7% for controls). TRV-2026-1587 In a crossover study of four commercial chest X-ray AI tools, AI assistance did not improve diagnostic accuracy for five readers reviewing 1200 patients at a single center. TRV-2026-0854 A systematic review found generative AI was 13.0 percentage points less accurate than physicians at radiology diagnosis. TRV-2026-1284 In Nepal, a study of 826 chest X-rays found only 17.89% of the AI's abnormal flags were correct at a threshold set for at least 95% sensitivity. TRV-2026-1265
Reviews of eye disease, echocardiography and heart ischemia AI say effects on safety, workflows and patient outcomes remain largely untested. TRV-2026-1490TRV-2026-1924TRV-2026-1434 A few encouraging results rest on very small samples: an AI app flagged all 13 occlusion heart attacks, but in only 33 selected, hard-to-read ECGs. TRV-2026-1923 In a study of 30 bowel disease patients, AI classifiers working on biopsy light scans did no better than chance (p = 0.51). TRV-2026-2047 A systematic review of rotator cuff tear AI found most diagnostic studies were rated high risk of bias, so reliability in real clinics is unproven. TRV-2026-1698
Whether AI help improves human readers is split: in a randomized crossover test, 21 physician assistants reached 92.9% accuracy on heart attack ECGs with AI versus 79.0% without it. TRV-2026-0651 A crossover study of four commercial chest X-ray tools found no accuracy gain for five readers. TRV-2026-0854 In a meta-analysis of urological cancer imaging, AI scored a pooled AUC of 0.91 against 0.83 for clinicians, yet was less sensitive than clinicians in the prostate and MRI subgroups. TRV-2026-0640
AI reading of scans, slides and ECGs often reports high accuracy for specific findings, mostly on retrospective data from one centre. TRV-2026-0676TRV-2026-0764TRV-2026-0640TRV-2026-1145TRV-2026-2312
Models forecasting survival, complications and recurrence usually beat standard scores on past data, often by modest margins. TRV-2026-1068TRV-2026-1828TRV-2026-1009TRV-2026-0919TRV-2026-1081
Accuracy often drops when models meet new hospitals, countries or scanners; reviews say external validation is sparse. TRV-2026-0638TRV-2026-1220TRV-2026-2058TRV-2026-1655TRV-2026-0817
General chatbots do well on some narrow tasks but trail specialists and clinicians on others. TRV-2026-2253TRV-2026-1679TRV-2026-1352TRV-2026-2004TRV-2026-0758TRV-2026-1284
Few studies measure whether AI changes decisions or patient outcomes, and the randomized evidence that exists is mixed. TRV-2026-2462TRV-2026-1587TRV-2026-0575TRV-2026-0651TRV-2026-0854TRV-2026-1490
Machine learning on biomarkers and spectra often separates patients from controls in small studies that have not been tested in clinics. TRV-2026-0728TRV-2026-0896TRV-2026-0616TRV-2026-1966TRV-2026-1256
Each record sits in one thread at most. A record with both a gain and a problem counts on both sides. 45 records fit none of these threads.
ace