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

Natural Language Processing (919) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (699) Electronic Health Records (1322) Human Factors and Usability Case Studies (1006) Generative Language Models Including ChatGPT (713) AI Language Models in Health Care (206)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66476, first published .
Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation

Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation

Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation

Authors of this article:

Mahshad Koohi Habibi Dehkordi1 Author Orcid Image ;   Yehoshua Perl1 Author Orcid Image ;   Fadi P Deek2 Author Orcid Image ;   Zhe He3 Author Orcid Image ;   Vipina K Keloth4 Author Orcid Image ;   Hao Liu5 Author Orcid Image ;   Gai Elhanan6 Author Orcid Image ;   Andrew J Einstein7 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

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  2. Hasani E, Richter S, Juratli T, Buszello C, Prem M, Willkommen S, Sandi-Gahun S, Eyüpoglu I, Polanski W. Layperson-Friendly AI Translation of Medical Documents to Improve Doctor–Patient Communication: Protocols for the AI-INFOCARE and AI-MEDTALK Randomized Controlled Trials. JMIR Research Protocols 2025;14:e77204 View