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

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Authors: Da Teng, Lihua Tan, Qiyuan Cao, Yanwei Xia, Na Zhang, Jiantao Li, Dan Zhao
Cited by: 15
References: 36

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.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Clinical Reasoning and Diagnostic Skills Radiology practices and education

Publication details

Journal / source
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Volume
9
Issue
1
Publication date
17 March 2026
Language
EN