Jurors are more likely to find radiologists at fault if AI detects an abnormality they miss, but transparency about AI error rates can mitigate this effect.
Key Details
- 1Study evaluated over 1,300 mock jurors using vignettes of missed brain bleeds or cancer diagnoses.
- 2Jurors sided with plaintiffs 72.9% (brain bleed) and 78.7% (cancer) when AI flagged missed findings, versus 56.3% and 65.2% with no AI.
- 3Disclosure of AI's false omission (1%) or false discovery (50%) rates reduced perceived radiologist liability.
- 4If both radiologist and AI missed abnormality, jurors were less likely to fault the radiologist (50% for brain bleed, 63.5% for cancer).
- 5Providing AI error rates had stronger mitigating effects for brain bleed cases than for cancer.
Why It Matters

Source
AuntMinnie
Related News

Study: Computer Vision Models Best LLMs in Chest CT Breast Abnormality Detection
Computer vision models (CVMs) surpass large language models (LLMs) in accurately labeling incidental breast abnormalities on chest CT scans.

Deep Learning Models Rival Radiologists for Pancreatic Cancer Detection on CT
Deep-learning models achieved comparable or superior accuracy to experienced radiologists in detecting pancreatic cancer on CT scans, especially for small tumors.

Radiology AI Devices at Elevated Risk for FDA Recalls, Study Finds
Radiology AI devices are more likely to face FDA recalls, largely due to deviations from intended use and incomplete clinical data.