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

New Methods (1140) Digital Health, Telehealth and e-Innovation in Clinical Settings (561) Decision Support for Health Professionals (1555) Adoption and Change Management of eHealth Systems (906) Development and Evaluation of Research Methods, Instruments and Tools (756) Methods and Instruments in Medical Informatics (195)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49570, first published .
The Potential of Evidence-Based Clinical Intake Tools to Discover or Ground Prevalence of Symptoms Using Real-Life Digital Health Encounters: Retrospective Cohort Study

The Potential of Evidence-Based Clinical Intake Tools to Discover or Ground Prevalence of Symptoms Using Real-Life Digital Health Encounters: Retrospective Cohort Study

The Potential of Evidence-Based Clinical Intake Tools to Discover or Ground Prevalence of Symptoms Using Real-Life Digital Health Encounters: Retrospective Cohort Study

Authors of this article:

Eden Avnat1, 2 Author Orcid Image ;   Michael Samin1, 3 Author Orcid Image ;   Daniel Ben Joya1, 3 Author Orcid Image ;   Eyal Schneider1, 4 Author Orcid Image ;   Elia Yanko5 Author Orcid Image ;   Dafna Eshel1 Author Orcid Image ;   Shahar Ovadia1 Author Orcid Image ;   Yossi Lev1 Author Orcid Image ;   Daniel Souroujon1, 2 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Avnat E, Levy M, Herstain D, Yanko E, Ben Joya D, Tzuchman Katz M, Eshel D, Laros S, Dagan Y, Barami S, Mermelstein J, Ovadia S, Shomron N, Shalev V, Abdulnour R. Performance of Large Language Models in Numerical Versus Semantic Medical Knowledge: Cross-Sectional Benchmarking Study on Evidence-Based Questions and Answers. Journal of Medical Internet Research 2025;27:e64452 View