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

Journals

  1. Seth I, Shadid O, Xie Y, Bacchi S, Cuomo R, Rozen W. Exploring the Role of Artificial Intelligence in Enhancing Surgical Education During Consultant Ward Rounds. Surgeries 2025;6(4):83 View
  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
  3. Koohi Habibi Dehkordi M, Perl Y, Deek F, Liu H. Fine-Tuning LLaMA2 for Summarizing Discharge Notes: Evaluating the Role of Highlighted Information. Big Data and Cognitive Computing 2025;10(1):4 View

Conference Proceedings

  1. Dehkordi M, Lu J, Perl Y, Deek F. 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Enhancing Patient Comprehension of Discharge Notes with a Retrieval-Augmented LLM Approach View
  2. Dehkordi M, Perl Y, Deek F. 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Optimizing Manual Review Using Machine Learning in Interface Terminology Curation for Automatic EHR Highlighting View