For how we live.
In the P4 trial, 1105 patients having coronary stents placed were randomised to planning by CT angiography or by intravascular ultrasound. Target-vessel failure at one year was 4.1% versus 4.6% — non-inferior.
A deep-learning model reads the pancreas on non-contrast CT taken for other reasons. It reached very high accuracy in large tests — none of them a prospective screening trial.
A four-image bitewing set delivers about 5 microsieverts, a panoramic scan 14 to 24. A cone-beam CT can be tens of times higher. The dose is small, but technique and how often you scan drive it more than anything.
Commercial full-body scans promise early detection to people with no symptoms. The data show they find something to chase in nearly half, and cancer in about one in a hundred.
It is a surgical emergency in which a twisted cord cuts off the testicle's blood supply. Salvage rates fall from over 90% inside six hours to single digits after two days, so speed to surgery is the whole game.
Every low-dose CT for lung cancer also images the heart. A 323-scan study found an AI tool matched expert calcium scoring almost exactly — and separate data show why that number carries weight.
A multicentre reader study found AI raised early-career dentists' caries sensitivity from 67% to 82% without more false positives. Independent testing shows the same tools still miss early lesions.
In a prospective African validation, TB-detection AI matched radiologists but neither hit the WHO sensitivity target. In an Indian field programme, AI-led screening lifted case-finding sharply.
A deep-learning system drew tumour targets and organs at risk automatically. It saved time and improved consistency — but oncologists still reviewed and edited the contours before treatment.
The first randomised trial to report interval cancers found AI non-inferior to double reading, with higher sensitivity at equal specificity. Across three European programmes the extra detection is about one per 1000.
Researchers built an AI pipeline to convert decades of narrative reports into one modern TNM staging system, reaching 95% accuracy for tumour classification in an expert-labelled test set.
TARGET-CTCA randomised 3,170 emergency patients whose troponin had cleared them of a heart attack. Outpatient CT angiography-guided care matched standard care on later infarction or cardiac death.
Where AI clot-detection was tested head-to-head, it still missed more emboli than radiology trainees, especially small peripheral ones. Its clearest win was cutting time to diagnosis.
Six randomised trials found no benefit from immediate imaging over usual care. A separate review explains the reason: most of what a scan finds is present in people with no pain at all.
Two 2026 studies found people with no ultrasound background produced diagnostic-quality echocardiograms after about a day of AI-guided training. Quality varied sharply by view, finding, and who was holding the probe.
Two 2026 meta-analyses pooled deep-learning systems reading retinal images for AMD. The accuracy looks near-perfect, but almost all of it comes from algorithms tested on their own data.
A transformer model trained on 17,119 scans beat expert and non-expert examiners on every metric and cut simulated referrals by 63%. The catch is who built it and the validation the rest of the field still lacks.
England ran the closest thing to a national test of clinical AI anyone has published. The software sites gained more, but the comparison sites were improving on their own, and that gap is the whole result.
Off-the-shelf features from two published models matched retinal scans to the right patient 78% to 86% of the time. Fine-tuned for the task, one model reached 99.5% on OCT.