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

Digital Health Reviews (3941) Natural Language Processing (1364) Digital Health Reporting Standards, Quality and Transparency in e-Research (262) Chatbots and Conversational Agents (1263) Artificial Intelligence (5142) Design and Evaluation of Patient Education Materials (183)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/88365, first published .
Doctor using computer and mouse at desk with stethoscope and patient chart

Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review

Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review

Authors of this article:

Cornelia Krenn1 Author Orcid Image ;   Christine Loder1 Author Orcid Image ;   Natalie Berger1 Author Orcid Image ;   Klaus Jeitler1, 2 Author Orcid Image ;   Thomas Semlitsch1 Author Orcid Image ;   Andrea Siebenhofer1, 3 Author Orcid Image ;   Denise Wilfling1, 4 Author Orcid Image
Article Authors Cited by (5) Tweetations Metrics

Journals

  1. Sun X, Liu Y, Wu X, Kong Y, Li Z, Yu G. Information quality, transparency, and readability of generative AI chatbot responses to parenting practice questions for infants and toddlers aged 0–3 years: A cross-sectional comparative study. DIGITAL HEALTH 2026;12 View
  2. Leonforte F, Cavallaro A, Nicosia V, Scapellato A, Gambera A, Genovese C, Mistretta A, Zanghì A, Basile G, Veroux M, Di Mattia P, Calabrò A, Tiralongo K, Gatthas Y. Artificial Intelligence in Medical and Surgical Information: A Systematic Review of Accuracy, Readability, Reliability, and Emotional Resonance. AI 2026;7(10):396 View
  3. Ahmad S, Hashim M, Abdullah A, Annesi C, Oslock W, Nijampurkar N, Abbas A, Hernandez-Marquez G, Harsono A, Hollis R, Rubyan M, Chu D. Artificial Intelligence in Patient Education: Closing the Gap Between Promise and Practice. The American Journal of Surgery 2026:117360 View
  4. Liang L, Zhang L, Wang H, Peng X, Zhang X, Liu S, Wang T, Qu P, Han J, Li Z. Reliability and readability of five large language models in answering patient-oriented questions about irritable bowel syndrome: a comparative study. Frontiers in Medicine 2026;13 View
  5. Mannhardt N, Bondi-Kelly E, Lam B, Mozannar H, Stanwyck C, Asiedu M, Buendia A, Urman T, Riaz I, Ricciardi C, Agrawal M, Ghassemi M, Sontag D. A mixed-methods evaluation of a language model-powered breast cancer note augmentation tool for patients. Scientific Reports 2026;16(1) View