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This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (5109) Quality/Credibility of eHealth Information and Trust Issues (939) Artificial Intelligence (4741) Computer-Aided Diagnosis (419) Applications of AI (948)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58666, first published .
Doctor working on laptop in medical office

Facilitating Trust Calibration in Artificial Intelligence–Driven Diagnostic Decision Support Systems for Determining Physicians’ Diagnostic Accuracy: Quasi-Experimental Study

Facilitating Trust Calibration in Artificial Intelligence–Driven Diagnostic Decision Support Systems for Determining Physicians’ Diagnostic Accuracy: Quasi-Experimental Study

Authors of this article:

Tetsu Sakamoto1 Author Orcid Image ;   Yukinori Harada1 Author Orcid Image ;   Taro Shimizu1 Author Orcid Image
Article Authors Cited by (14) Tweetations Metrics

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  2. Chaudhry Z, Choudhury A. US Occupational Medicine Clinicians’ Perceptions and Practices With Respect to Artificial Intelligence Large Language Models. Journal of Occupational & Environmental Medicine 2026;68(3):189 View
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  5. Bair H, Djulbegovic M, Taylor Gonzalez D. The clinician as gatekeeper in the age of “deep research”. DIGITAL HEALTH 2026;12 View
  6. Babker A. Clinical Safety and Legal Accountability of Artificial Intelligence in Hematology Diagnostic Laboratories: Risks, Failure Modes, Medicolegal Challenges, and Budgeting for Implementation. Journal of Applied Hematology 2026;17(2):85 View
  7. Pan H, Liu J, Liu S. Enhancing Physician Resilience to Generative AI: Multilevel Framework for Shared Authority, Verification, and Skill Preservation. Journal of Medical Internet Research 2026;28:e88058 View
  8. Velasco L, Wang W. Trust typology of medical doctors in artificial intelligence. Procedia Computer Science 2026;284:113 View
  9. Dong Y, Jiang P, Zhou X. Artificial intelligence-based software as a medical device: regulatory evolution toward continuous oversight. Medical Review 2026 View
  10. Castano-Villegas N, Llano I, Villa M, Zea J, Velasquez L. Clinical Med students’ validation of Arkangel AI: Are their responses any better when supported by the AI?. Intelligence-Based Medicine 2026;15:100447 View

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  1. Alrefaie K, Madlol B, Atallah O. Medical Education in the Age of Digital Transformation. View
  2. Anderle L, Abidi A, Ünlü S, Steidle O, Janßen T, Petzold T. Chatbots and Human-Centered AI. View

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  2. Dudeja P, Mishra D, Mathur R, Gupta S, Kumar D, Hemanth J. 2026 International Conference on Intelligent Engineering and Next-Gen Healthcare Systems (IEHNS). Calibrated and Uncertainty-Aware Heart Disease Prediction Using Ensemble Learning View