Version 1 of this summary, as approved on . It weighed 277 gains, 84 problems and 92 Traces. This is the current version. See the topic as it stands today
Most records report AI performing well on scans, slides, signals and patient data, usually in retrospective or internal tests. TRV-2026-0486TRV-2026-1490 Few records show that patients did better, and reviews repeatedly note missing outside validation and missing outcome data. TRV-2026-1924TRV-2026-1490TRV-2026-0486
A randomized trial of 300 people with suspected inherited retinal disease shows specialists using an AI tool named the right gene category in their top four answers 81.8% of the time versus 53.1% alone. TRV-2026-0575 In a randomized crossover test, 21 physician assistants reading 50 ECGs reached 92.9% accuracy on heart attack diagnosis with AI versus 79.0% without it, though they took longer. TRV-2026-0651 A systematic review shows AI detected STEMI heart attacks on ECGs with higher sensitivity than clinicians (0.91 versus 0.78), and another review shows AI help raised doctors' fracture detection to 87% from 73%. TRV-2026-2312TRV-2026-1145 A meta-analysis of urological cancer imaging found AI reached a pooled AUC of 0.91 against 0.83 for clinicians, mostly in retrospective studies. TRV-2026-0640
In a randomized trial of 171 veterans after a heart failure hospitalization, AI-guided monitoring did not significantly cut rehospitalization (16.3% versus 18.7% for controls). TRV-2026-1587 A review of 48 studies found generative AI was 13.0 percentage points less accurate than physicians at radiology diagnosis. TRV-2026-1284 Four commercial chest X-ray tools did not improve five readers' accuracy across 1200 patients, and eight dental AI systems caught only 32.7% to 48.7% of decay next to fillings. TRV-2026-0854TRV-2026-1673 A study of 826 chest X-rays in Nepal found only 17.89% of the AI's abnormal flags were correct at a threshold set for high sensitivity. TRV-2026-1265
Many models lose accuracy outside the hospital that built them: a review of knee arthritis AI found median AUC fell to 0.75 from 0.81 on outside data, and Alzheimer's brain-scan AI performed worse outside single centers. TRV-2026-0638TRV-2026-1244 Reviews of heart, lung and eye AI say most studies are retrospective and single-center, with little data on workflow, patient outcomes or cost. TRV-2026-1434TRV-2026-1017TRV-2026-1490 Small pilots add little certainty: a 13-patient simulation suggested patient feedback cut false seizure alarms, while in about 60 children none of seven models using brain-scan signals beat chance at spotting autism. TRV-2026-1616TRV-2026-2016 Performance across patient groups is rarely tested; cuffless blood pressure estimates were less accurate for older and Black ICU patients, and a Brazilian sick-leave model was less accurate for Black workers. TRV-2026-1497TRV-2026-1528
At one hospital, an AI model diagnosed chronic atrophic gastritis better than 15 novice and 16 expert endoscopists, and in 33 hard-to-read ECGs an AI app flagged all 13 occlusion heart attacks. TRV-2026-1937TRV-2026-1923 Other records point the other way: on 1000 prostate MRI cases AI did not confirm non-inferiority against routine practice, and dentists predicted tooth survival more accurately than an AI model (75.6% and 74.9% versus 69.2%). TRV-2026-0402TRV-2026-0856 A pooled analysis found the edge depends on the task, with AI less sensitive than clinicians in the prostate cancer and MRI subgroups. TRV-2026-0640
Tools that read scans, X-rays and slides often score well in internal tests and lose accuracy at outside sites, and few have been tried prospectively on patients. TRV-2026-0257TRV-2026-1244TRV-2026-0638
Models built on slides, scans and gene scores sort cancer patients by risk, mostly in retrospective data from one center. TRV-2026-1589TRV-2026-1865TRV-2026-2138TRV-2026-0806
A randomized trial of 14,665 adults found AI-guided ECG monitoring picked up slightly more new atrial fibrillation, though the authors call it statistically fragile, and a review found AI-read ECGs detect weak pumping in people without symptoms. TRV-2026-2462TRV-2026-1837
General chatbots trail clinicians on image-based diagnosis and often miss detail, though some do better on text tasks. TRV-2026-1284TRV-2026-2253TRV-2026-2004TRV-2026-1679
Machine learning on urine, breath, blood and brain-wave signals often reports high accuracy, but in small or internal samples. TRV-2026-1256TRV-2026-0616TRV-2026-0896TRV-2026-0801
Hospital prediction models catch many events but raise false alarms or lose accuracy on outside data. TRV-2026-2093TRV-2026-1733TRV-2026-1655
Each record sits in one thread at most. A record with both a gain and a problem counts on both sides. 108 records fit none of these threads.
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