Impact of AI misinformation on diagnostic accuracy and confidence calibration in novice medical students

For novice medical learners, do the benefits of correct AI explanations outweigh the risks of plausible misinformation? In a randomized trial with 111 students, we found they do not. Our results reveal a significant and problematic asymmetry: misleading AI explanations significantly degraded diagnostic accuracy, while correct explanations offered no significant improvement over a no-explanation control. Misleading explanations reduced diagnostic accuracy and showed no evidence of confidence calibration, such that confidence did not reliably distinguish correct from incorrect responses. This study provides crucial empirical evidence that, without proper safeguards, the harm caused by AI-generated falsehoods in this population and task is more potent and robust than the benefit derived from correct guidance. This finding highlights a fundamental safety challenge for AI in medical education, demanding a strategic pivot towards building learners' critical appraisal skills. Trial registration: Chinese Clinical Trial Registry (ChiCTR), ChiCTR2500111932, registered on 7 November 2025.

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Publication Details

Journal
npj Digital Medicine
Published
2026-03-17
DOI
https://doi.org/10.1038/s41746-026-02547-z
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Impact of AI misinformation on diagnostic accuracy and confidence calibration in novice medical students

Qiyuan Cao, Da Teng, Na Zhang, Dan Zhao et al.
npj Digital Medicine
Artificial Intelligence in Healthcare and Education
article

Impact of AI misinformation on diagnostic accuracy and confidence calibration in novice medical students

Qiyuan Cao, Da Teng, Na Zhang, Dan Zhao, Yanwei Xia, Lihua Tan, Jiantao Li
article en

Abstract

For novice medical learners, do the benefits of correct AI explanations outweigh the risks of plausible misinformation? In a randomized trial with 111 students, we found they do not. Our results reveal a significant and problematic asymmetry: misleading AI explanations significantly degraded diagnostic accuracy, while correct explanations offered no significant improvement over a no-explanation control. Misleading explanations reduced diagnostic accuracy and showed no evidence of confidence calibration, such that confidence did not reliably distinguish correct from incorrect responses. This study provides crucial empirical evidence that, without proper safeguards, the harm caused by AI-generated falsehoods in this population and task is more potent and robust than the benefit derived from correct guidance. This finding highlights a fundamental safety challenge for AI in medical education, demanding a strategic pivot towards building learners' critical appraisal skills. Trial registration: Chinese Clinical Trial Registry (ChiCTR), ChiCTR2500111932, registered on 7 November 2025.

npj Digital Medicine
Beijing Institute of Petrochemical Technology (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Netaji Subhas National Institute of Sports (IN), Chinese PLA General Hospital (CN), National Cancer Center (US), National Clinical Research Center for Digestive Diseases (CN), Qinghai No.3 People's Hospital (CN), Qinghai Provincial Peoples Hospital (CN)
Quality Education
Openalex Percentile: Top 21%
Artificial Intelligence in Healthcare and Education
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