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
As AI tools become more integrated into radiology workflows, awareness of their error profiles and transparent communication may have significant legal implications. The findings can inform how radiologists document and present AI results to reduce liability risk in diagnostic misses.
Source
AuntMinnie
Related News
•AuntMinnie
Toronto Study: LLMs Must Cite Sources for Radiology Decision Support
University of Toronto researchers found that large language models (LLMs) such as DeepSeek V3 and GPT-4o offer promising support for radiology decision-making in pancreatic cancer when their recommendations cite guideline sources.
•AuntMinnie
AI Model Using Mammograms Enhances Five-Year Breast Cancer Risk Assessment
A new image-only AI model more accurately predicts five-year breast cancer risk than breast density alone, according to multinational research presented at RSNA 2025.
•Radiology Business
AI Model Uses CT Scans to Reveal Biomarker for Chronic Stress
Researchers developed an AI model to measure chronic stress using adrenal gland volume on routine CT scans.