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

Formative Evaluation of Digital Health Interventions (3383) Clinical Informatics (1397) Artificial Intelligence (2280) Digital Biomarkers and Digital Phenotyping (307) Development and Evaluation of Research Methods, Instruments and Tools (785) Machine Learning (2193) Decision Support for Health Professionals (1603) Clinical Information and Decision Making (2029) Computer-Aided Diagnosis (270) AI Applications in Biomedical Engineering (20)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53985, first published .
Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study

Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study

Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study

Authors of this article:

Yukinori Harada1, 2 Author Orcid Image ;   Tetsu Sakamoto1 Author Orcid Image ;   Shu Sugimoto3 Author Orcid Image ;   Taro Shimizu1 Author Orcid Image
Article Authors Cited by (9) Tweetations (6) Metrics

Journals

  1. Hirosawa T, Harada Y, Tokumasu K, Ito T, Suzuki T, Shimizu T. Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study. JMIR Medical Informatics 2024;12:e63010 View
  2. Hirosawa T, Harada Y, Tokumasu K, Shiraishi T, Suzuki T, Shimizu T. Comparative Analysis of Diagnostic Performance: Differential Diagnosis Lists by LLaMA3 Versus LLaMA2 for Case Reports. JMIR Formative Research 2024;8:e64844 View
  3. Pankow A, Meißner-Bendzko N, Kaufeld J, Fouquette L, Cotte F, Gilbert S, Türk E, Das A, Terkamp C, Burmester G, Wagner A. Medical Expert Knowledge Meets AI to Enhance Symptom Checker Performance for Rare Disease Identification in Fabry Disease: Mixed Methods Study. JMIR AI 2025;4:e55001 View
  4. Chen M, Wu Y, Ma J, Jia X, Gao C, Zhao F, Qiao Y. Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage. npj Digital Medicine 2026 View
  5. Reese J, Chimirri L, Bridges Y, Danis D, Caufield J, Gargano M, Kroll C, Schmeder A, Liu F, Wissink K, McMurry J, Graefe A, Niyonkuru E, Korn D, Casiraghi E, Valentini G, Jacobsen J, Haendel M, Smedley D, Mungall C, Robinson P. Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools. European Journal of Human Genetics 2026 View
  6. Borat S, Chowdhury S. Artificial intelligence-driven clinical decision support systems for precision oncology: A comprehensive review. In Silico Research in Biomedicine 2026;2:100265 View

Books/Policy Documents

  1. Herrera Montano I, Góngora Alonso S, Martínez Licort R, Sainz de Abajo B, de la Torre Díez I, Miramontes González J, Simón Pérez C, Briongos Figuero L, Corral Gudino L. Proceedings of 20th Iberian Conference on Information Systems and Technologies (CISTI 2025). View

Conference Proceedings

  1. Burle R, Gundewar S, Gaurkhede S, Fulkar B. 2025 International Conference on Electronics and Renewable Systems (ICEARS). Conversational Symptom Checker Chatbot for Disease Prediction and Suggesting Nearby Hospitals using Machine Learning and Location Services View
  2. Sharma G, Ojha M, Prinima Gupta D, Rouf A, Rathore Y, kumar rao S. 2025 2nd International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI). AI-Based Symptom Checker Tools: Towards Accessible and Reliable Primary Healthcare Support View