ArticleFrontiers in public health2026
Safety, accuracy, empathy, information quality, and readability of publicly accessible LLM-based chatbots for traumatic brain injury and concussion questions: a cross-sectional comparative study.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Large language model (LLM)-based chatbots are increasingly used by the public to obtain health information, but their performance in answering questions related to traumatic brain injury (TBI) and concussion remains unclear. This study evaluated five publicly accessible LLM-based chatbots across safety, accuracy, empathy, information quality, transparency, and readability. Methods: Sixty-five English-language, public-facing questions about TBI and concussion were submitted to ChatGPT, Gemini, Copilot, DeepSeek, and Doubao, generating 325 responses. Five blinded independent raters assessed the generated responses using guideline-informed criteria and established tools, including DISCERN, EQIP, JAMA benchmark criteria, the Global Quality Score, and readability indices. Results: Inter-rater agreement was high. The proportions of responses rated as safe ranged from 89.2 to 95.4%, and all models achieved a median accuracy score of 4.00. However, 27 responses were classified as potentially harmful, mainly because of under-triage, overly reassuring advice regarding imaging findings, premature return-to-activity or driving guidance, and insufficient pediatric caution. Accuracy differences were statistically significant but small, whereas empathy, information quality, transparency, and readability showed clearer model-level variation. DeepSeek produced the easiest-to-read responses. Conclusion: LLM-based chatbots generated responses that were generally rated as safe and informative by expert evaluators, but potentially harmful advice and readability problems remained. These findings characterize expert-rated response performance and do not establish patient comprehension, educational effectiveness, or clinical benefit. LLM-based chatbots may have potential as supplementary sources of patient-facing health information, but they should not replace professional medical evaluation.
Indexed as
Identifiers
What OpenQuestion holds
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.