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<p>Artificial intelligence (AI) is rapidly changing the legal landscape of radiology. Here we examined whether the manner in which AI is integrated into the radiologist workflow impacts the perception of legal liability among mock jurors in the USA. Participants (<i>n</i> = 282) read about a hypothetical malpractice case where a patient suffered irreversible brain damage because a radiologist failed to detect a brain bleed from a computerized tomography (CT) scan, even though AI correctly identified the CT as abnormal. In the single-read condition, the radiologist interpreted the CT once after seeing AI feedback. In the double-read condition, the radiologist interpreted the CT twice, first without AI and then with AI feedback. Participants were asked whether the radiologist met their duty of care (yes or no). Participants were more likely to side with the plaintiff in the single-read versus the double-read condition (74.7% versus 52.9%), <i>P</i> = 0.0002 (odds ratio 2.6; 95% confidence interval 1.6, 4.4). This suggests that the penalty for disagreeing with correct AI can be mitigated when images are interpreted twice.</p>

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The radiologist–AI workflow and the risk of medical malpractice claims

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Michael H. Bernstein,
  • Brian Sheppard,
  • Michael A. Bruno,
  • Parker S. Lay,
  • Grayson L. Baird

摘要

Artificial intelligence (AI) is rapidly changing the legal landscape of radiology. Here we examined whether the manner in which AI is integrated into the radiologist workflow impacts the perception of legal liability among mock jurors in the USA. Participants (n = 282) read about a hypothetical malpractice case where a patient suffered irreversible brain damage because a radiologist failed to detect a brain bleed from a computerized tomography (CT) scan, even though AI correctly identified the CT as abnormal. In the single-read condition, the radiologist interpreted the CT once after seeing AI feedback. In the double-read condition, the radiologist interpreted the CT twice, first without AI and then with AI feedback. Participants were asked whether the radiologist met their duty of care (yes or no). Participants were more likely to side with the plaintiff in the single-read versus the double-read condition (74.7% versus 52.9%), P = 0.0002 (odds ratio 2.6; 95% confidence interval 1.6, 4.4). This suggests that the penalty for disagreeing with correct AI can be mitigated when images are interpreted twice.