ArticleDigital health
An evaluation of the reliability and readability of large language models in the dissemination of traumatic brain injury information.
Article in Digital health. 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.
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Who cites it
1 citing paper in PubMed.
- Information quality, readability, and empathy of AI-generated public mental health information: a comparative evaluation of eight large language models.Frontiers in public health · 2026Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: To compare the reliability and readability of responses from Generative Pre-trained Transformer versions 3.5 (GPT-3.5) and 4.0 (GPT-4.0) on traumatic brain injury (TBI) topics against Model Systems Knowledge Translation Center (MSKTC) fact sheets. Methods: This study analyzed responses from GPT-3.5 and GPT-4.0 for accuracy, comprehensiveness, and readability against MSKTC fact sheets, incorporating a correlation analysis between reliability and readability scores. Results: Findings showed an improvement in reliability from GPT-3.5 (mean score = 3.21) to GPT-4.0 (mean score = 3.63), indicating better accuracy and completeness in the latter. Despite advancements, responses generally remained accurate but not fully comprehensive. Readability comparisons found the MSKTC fact sheets were significantly more reader-friendly compared to responses from both artificial intelligence (AI) versions, with no strong correlation between reliability and readability. Conclusion: The study highlights progress in AI-generated information on TBI from GPT-3.5 to GPT-4.0 in terms of reliability. However, challenges persist in matching the readability of standard patient education materials, emphasizing the need for future AI developments to focus on enhancing understandability alongside accuracy.
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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.