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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.
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.
Commercial software reads an X-ray for breaks about as well as a radiologist. A prospective paediatric emergency study found that accuracy did not translate into fewer missed-fracture recalls.
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.
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.
GE's Critical Care Suite gains an algorithm that flags misplaced enteric tubes on a chest X-ray — a complication that reviews of the practice have linked to respiratory harm and, in some cases, death.
A new audit of China's regulatory record finds a market concentrated in radiology, dominated by deep learning, and clustered in four cities. It also finds the approval curve flattening.
A systematic review of every AI device authorised from 1995 to June 2024 found 97% cleared through the 510(k) pathway, which does not require independent clinical data on performance or safety.
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.
A draft-reporting model cut documentation time 15.5% across 23,960 radiographs without changing report quality. A separate reader study found AI assistance raised prostate MRI accuracy by 3.3 percentage points.