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

Viewpoints and Perspectives (507) Knowledge Translation and Implementation Science (393) Symptom Checkers (28)

Published on in Vol 24, No 10 (2022): October

This is a member publication of Imperial College London (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37408, first published .
Online Symptom Checkers: Recommendations for a Vignette-Based Clinical Evaluation Standard

Online Symptom Checkers: Recommendations for a Vignette-Based Clinical Evaluation Standard

Online Symptom Checkers: Recommendations for a Vignette-Based Clinical Evaluation Standard

Authors of this article:

Annabelle Painter1 Author Orcid Image ;   Benedict Hayhoe1 Author Orcid Image ;   Eva Riboli-Sasco1 Author Orcid Image ;   Austen El-Osta1 Author Orcid Image
Article Authors Cited by (13) Tweetations (18) Metrics

Journals

  1. Mehrali T, Cotte F, Wicks P, Gilbert S. Response to Ben-Shabat et al.’s “Assessing data gathering of chatbot based symptom checkers – A clinical vignettes study”. International Journal of Medical Informatics 2023;170:104961 View
  2. Kopka M, Feufel M, Berner E, Schmieding M. How suitable are clinical vignettes for the evaluation of symptom checker apps? A test theoretical perspective. DIGITAL HEALTH 2023;9 View
  3. Riboli-Sasco E, El-Osta A, Alaa A, Webber I, Karki M, El Asmar M, Purohit K, Painter A, Hayhoe B. Triage and Diagnostic Accuracy of Online Symptom Checkers: Systematic Review. Journal of Medical Internet Research 2023;25:e43803 View
  4. Peven K, Wickham A, Wilks O, Kaplan Y, Marhol A, Ahmed S, Bamford R, Cunningham A, Prentice C, Meczner A, Fenech M, Gilbert S, Klepchukova A, Ponzo S, Zhaunova L. Assessment of a Digital Symptom Checker Tool's Accuracy in Suggesting Reproductive Health Conditions: Clinical Vignettes Study. JMIR mHealth and uHealth 2023;11:e46718 View
  5. Hammoud M, Douglas S, Darmach M, Alawneh S, Sanyal S, Kanbour Y. Evaluating the Diagnostic Performance of Symptom Checkers: Clinical Vignette Study. JMIR AI 2024;3:e46875 View
  6. Hirosawa T, Harada Y, Tokumasu K, Ito T, Suzuki T, Shimizu T. Evaluating ChatGPT-4’s Diagnostic Accuracy: Impact of Visual Data Integration. JMIR Medical Informatics 2024;12:e55627 View
  7. Meczner A, Cohen N, Qureshi A, Reza M, Sutaria S, Blount E, Bagyura Z, Malak T. Controlling Inputter Variability in Vignette Studies Assessing Web-Based Symptom Checkers: Evaluation of Current Practice and Recommendations for Isolated Accuracy Metrics. JMIR Formative Research 2024;8:e49907 View
  8. Hirosawa T, Harada Y, Mizuta K, Sakamoto T, Tokumasu K, Shimizu T. Evaluating ChatGPT-4’s Accuracy in Identifying Final Diagnoses Within Differential Diagnoses Compared With Those of Physicians: Experimental Study for Diagnostic Cases. JMIR Formative Research 2024;8:e59267 View
  9. Harada Y, Sakamoto T, Sugimoto S, Shimizu T. Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study. JMIR Formative Research 2024;8:e53985 View
  10. Liu V, Kaila M, Koskela T. Triage Accuracy and the Safety of User-Initiated Symptom Assessment With an Electronic Symptom Checker in a Real-Life Setting: Instrument Validation Study. JMIR Human Factors 2024;11:e55099 View
  11. Kopka M, Feufel M. Software symptomcheckR: an R package for analyzing and visualizing symptom checker triage performance. BMC Digital Health 2024;2(1) View
  12. Hirosawa T, Harada Y, Mizuta K, Sakamoto T, Tokumasu K, Shimizu T. Diagnostic performance of generative artificial intelligences for a series of complex case reports. DIGITAL HEALTH 2024;10 View
  13. Kopka M, Feufel M. Statistical refinement of patient-centered case vignettes for digital health research. Frontiers in Digital Health 2024;6 View