ArticleNPP - digital psychiatry and neuroscience2025
Language models in digital psychiatry: challenges with simplification of healthcare materials.
Article in NPP - digital psychiatry and neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Linguistic hurdles in healthcare, such as complex language, significantly affect patient outcomes, including satisfaction with interaction, comprehension of healthcare materials, and engagement with the healthcare system. Reducing these hurdles has been a focus in healthcare delivery, as they significantly hinder patient engagement and adherence to treatments. The growing use of large language models (LLMs) in healthcare opens the possibility to reduce these linguistic hurdles. This study evaluates the ability of five prominent LLMs-GPT-3.5, GPT-4, GPT-4o, LLaMA-3, and Mistral-to simplify healthcare information to the standard recommended by the American Journal of Medicine. Our results indicate that while LLMs can approximate targeted reading levels, their outputs are inconsistent, with significant variability in reading level and deviation from the topic, making them unsuitable for deployment in healthcare settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.