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
Deep Learning AI Outperforms Radiologists in Detecting ENE on CT
A deep learning tool, DeepENE, exceeded radiologist performance in identifying lymph node extranodal extension in head and neck cancers using preoperative CT scans.
•Radiology Business
Patients Favor AI in Imaging Diagnostics, Hesitate on Triage Use
Survey finds most patients support AI in diagnostic imaging but are reluctant about its use in triage decisions.
•AuntMinnie
AI Projected to Reshape Radiologist Workload But Not Eliminate Jobs
Stanford researchers predict AI could reduce radiologist hours by up to 49% over the next five years, though workforce size is likely to remain stable due to rising imaging demand.