How Following Medical Artificial Intelligence Advice Can Mitigate Malpractice Liability: Cross-National Insights from a Randomized Trial

Artificial intelligence (AI) increasingly influences clinical decision-making, yet its recommendations may diverge from standard care. Although malpractice concerns are thought to discourage physicians from following AI advice, experimental evidence from the United States suggests the opposite: lay jurors are more likely to hold physicians liable when they reject AI recommendations. Whether this pattern extends to systems in which court-appointed experts, not lay jurors, determine liability remains unknown. <b>Methods:</b> To examine how physicians and laypeople in expert-based and lay-juror legal systems evaluate physicians’ acceptance or rejection of AI recommendations, particularly when those recommendations deviate from standard care, we designed a randomized vignette study: a 2 × 2 factorial design varying the AI recommendation (standard vs. nonstandard care) and a fictional physician’s decision (accept vs. reject). The study was conducted online in 2023 among nationally representative samples of U.S. and German adults and from 2023 to 2024 among German physicians. In total, 387 German physicians, 2291 U.S. adults, and 2283 German adults participated; those not completing the survey or failing attention checks were excluded per preregistered criteria. Participants were randomly assigned to 1 of 4 vignettes, varying the AI recommendation (standard vs. nonstandard care) and physician’s decision (accept vs. reject). The reasonableness of the fictional physician’s decision was measured, rated by participants on a Likert scale. <b>Results:</b> Analysis, following preregistered exclusion criteria, included 248 German physicians, 1202 U.S. adults, and 1358 German adults. Physicians accepting standard-care AI recommendations were rated more reasonable than those rejecting them (U.S. laypeople: <i>t</i> = 5.36; 95% CI, 0.45–0.97; <i>P</i> &lt; 0.001; German physicians: <i>t</i> = 2.47; 95% CI, 0.14–1.30; <i>P</i> = 0.02; German laypeople: <i>t</i> = 4.14; 95% CI, 0.27–0.76; <i>P</i> &lt; 0.001). Ratings of physicians accepting versus rejecting AI nonstandard-care recommendations were statistically equivalent. Equivalence was tested at an α-value of 0.05 using a two 1-sided tests procedure, reported with 90% CIs per standard convention (U.S. laypeople: <i>t</i> = –4.90; 90% CI, –0.1 to 0.36; <i>P</i> &lt; 0.001; German physicians: <i>t</i> = –1.76; 90% CI, –0.12 to 0.67; <i>P</i> = 0.04; German laypeople: <i>t</i> = 5.35; 90% CI, –0.35 to 0.06; <i>P</i> &lt; 0.001). <b>Conclusion:</b> Across the United States and Germany, samples representative of lay jurors and court-appointed experts viewed accepting standard-care AI advice as more reasonable, whereas accepting or rejecting nonstandard-care AI advice was judged similarly. Contrary to predictions, malpractice liability regimes do not necessarily pose a barrier to AI use in precision medicine.

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Publication Details

Journal
Journal of Nuclear Medicine
Published
2026-06-04
DOI
https://doi.org/10.2967/jnumed.126.272292
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

How Following Medical Artificial Intelligence Advice Can Mitigate Malpractice Liability: Cross-National Insights from a Randomized Trial

Alessandro Tacconelli, Alexander Stremitzer, Jakob Merane, Aileen Nielsen et al.
Journal of Nuclear Medicine
Artificial Intelligence in Healthcare and Education
article

How Following Medical Artificial Intelligence Advice Can Mitigate Malpractice Liability: Cross-National Insights from a Randomized Trial

Alessandro Tacconelli, Alexander Stremitzer, Jakob Merane, Aileen Nielsen, Björn Hackanson, Kevin Tobia
article en

Abstract

Artificial intelligence (AI) increasingly influences clinical decision-making, yet its recommendations may diverge from standard care. Although malpractice concerns are thought to discourage physicians from following AI advice, experimental evidence from the United States suggests the opposite: lay jurors are more likely to hold physicians liable when they reject AI recommendations. Whether this pattern extends to systems in which court-appointed experts, not lay jurors, determine liability remains unknown. Methods: To examine how physicians and laypeople in expert-based and lay-juror legal systems evaluate physicians’ acceptance or rejection of AI recommendations, particularly when those recommendations deviate from standard care, we designed a randomized vignette study: a 2 × 2 factorial design varying the AI recommendation (standard vs. nonstandard care) and a fictional physician’s decision (accept vs. reject). The study was conducted online in 2023 among nationally representative samples of U.S. and German adults and from 2023 to 2024 among German physicians. In total, 387 German physicians, 2291 U.S. adults, and 2283 German adults participated; those not completing the survey or failing attention checks were excluded per preregistered criteria. Participants were randomly assigned to 1 of 4 vignettes, varying the AI recommendation (standard vs. nonstandard care) and physician’s decision (accept vs. reject). The reasonableness of the fictional physician’s decision was measured, rated by participants on a Likert scale. Results: Analysis, following preregistered exclusion criteria, included 248 German physicians, 1202 U.S. adults, and 1358 German adults. Physicians accepting standard-care AI recommendations were rated more reasonable than those rejecting them (U.S. laypeople: t = 5.36; 95% CI, 0.45–0.97; P < 0.001; German physicians: t = 2.47; 95% CI, 0.14–1.30; P = 0.02; German laypeople: t = 4.14; 95% CI, 0.27–0.76; P < 0.001). Ratings of physicians accepting versus rejecting AI nonstandard-care recommendations were statistically equivalent. Equivalence was tested at an α-value of 0.05 using a two 1-sided tests procedure, reported with 90% CIs per standard convention (U.S. laypeople: t = –4.90; 90% CI, –0.1 to 0.36; P < 0.001; German physicians: t = –1.76; 90% CI, –0.12 to 0.67; P = 0.04; German laypeople: t = 5.35; 90% CI, –0.35 to 0.06; P < 0.001). Conclusion: Across the United States and Germany, samples representative of lay jurors and court-appointed experts viewed accepting standard-care AI advice as more reasonable, whereas accepting or rejecting nonstandard-care AI advice was judged similarly. Contrary to predictions, malpractice liability regimes do not necessarily pose a barrier to AI use in precision medicine.

Journal of Nuclear Medicine
University of Massachusetts Dartmouth (US), Harvard University (US), University of Augsburg (DE), Georgetown University (US), University Medical Center Freiburg (DE), University of Massachusetts Boston (US), ETH Zurich (CH)
Universitätsklinikum Köln, Universitätsmedizin der Johannes Gutenberg-Universität Mainz
Reduced inequalities
Openalex Percentile: Top 9%
Artificial Intelligence in Healthcare and Education
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