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

Consumer & Patient Education and Shared-Decision Making (955) Artificial Intelligence (2462) Design and Evaluation of Patient Education Materials (139) Generative Language Models Including ChatGPT (887) Applications of AI (385)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67143, first published .
Patient in hospital gown reviews medical data on holographic display.

What’s Going On With Me and How Can I Better Manage My Health? The Potential of GPT-4 to Transform Discharge Letters Into Patient-Centered Letters to Enhance Patient Safety: Prospective, Exploratory Study

What’s Going On With Me and How Can I Better Manage My Health? The Potential of GPT-4 to Transform Discharge Letters Into Patient-Centered Letters to Enhance Patient Safety: Prospective, Exploratory Study

Authors of this article:

Felix Eisinger1 Author Orcid Image ;   Friederike Holderried2 Author Orcid Image ;   Moritz Mahling3 Author Orcid Image ;   Christian Stegemann–Philipps2 Author Orcid Image ;   Anne Herrmann–Werner2 Author Orcid Image ;   Eric Nazarenus2 Author Orcid Image ;   Alessandra Sonanini2 Author Orcid Image ;   Martina Guthoff1 Author Orcid Image ;   Carsten Eickhoff4 Author Orcid Image ;   Martin Holderried3 Author Orcid Image
Article Authors Cited by (14) Tweetations Metrics

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  2. Kimberger O, Rodemund N, Maleczek M. Einsatz von Large Language Models in der Anästhesie und Intensivmedizin. Anästhesie Nachrichten 2025;7(2):107 View
  3. Armstrong V, Grabeel K, Heidel R, McGillicuddy C, Zmijewski P, Murdock P, Lew J, Vaghaiwalla T. Patient Educational Materials for Pheochromocytoma Exceed Recommended Readability Level: An Analysis Across Three Independent Reading Instruments. Journal of Cancer Education 2026;41(2):333 View
  4. Breuer T, Frihat S, Fuhr N, Lewandowski D, Schaer P, Schenkel R. Large Language Models for Information Retrieval: Challenges and Chances. Datenbank-Spektrum 2025;25(2):71 View
  5. Li B, Huang Y, Mao W, Liu J, Ma Q. Applications and Prospects of Digital Health Technologies in Cardiovascular Nursing: Smart Devices, Remote Monitoring, and Personalized Care. Journal of Multidisciplinary Healthcare 2025;Volume 18:6275 View
  6. Holderried F, Sonanini A, Stegemann–Philipps C, Herrmann–Werner A, Spitzer P, Guthoff M, Heyne N, Sering K, Holderried M, Eisinger F. Impact of GPT-4–Generated Discharge Letters on Patients’ Medical Comprehension: Prospective Crossover Study. Journal of Medical Internet Research 2026;28:e81243 View
  7. Vavekanand R, Ali Laghari A, Kumar T. Applications and limitations of large language models to integrate medical context: a comprehensive review. Iran Journal of Computer Science 2026;9(1) View
  8. Wang M, Ma H, Piao M. Effectiveness of large language models in preoperative and discharge education: a systematic review based on an evaluation framework. npj Digital Medicine 2026;9(1) View
  9. Verma D, Darlan D, Mallipeddi R. Multi-Objective Optimization of Large Language Model Summaries for Clinical Text Narratives. IEEE Access 2026;14:13597 View
  10. Abdelhamid M, Howell P, Singh D. Acceptance of Smart Contracts in Patients Receiving Primary Care: Exploratory Study. JMIR Formative Research 2026;10:e82237 View
  11. Reimers S. Generative AI in health sciences education and practice. Part 3: supporting written communication. British Journal of Cardiac Nursing 2026;21(4):1 View
  12. Krenn C, Loder C, Berger N, Jeitler K, Semlitsch T, Siebenhofer A, Wilfling D. Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review. Journal of Medical Internet Research 2026;28:e88365 View
  13. Ayre J, Shao L, Dunn A. Why We Need Patients and Community at the Center of AI Health Communication Research. Journal of Medical Internet Research 2026;28:e97577 View
  14. Chuan C, Tang J, Yang Z, Chimbaru R. Evaluating AI-Mediated Health Communication via Large Language Model–Based Frequently Asked Questions Rewriting to Foster Clinical Trial Participation: Comparative Survey Study. JMIR AI 2026;5:e87446 View