Although all three large language models generally produced reliable content, ChatGPT-5.2 and DeepSeek V3.2 showed stronger clinical accuracy performance, and because of the risk of incorrect information being generated, the use of large language models by the older population should preferably be done under expert supervision.
Abstract
Introduction: Older adults in geriatric rehabilitation are increasingly
turning to internet-based resources and large language models for healthrelated information outside of clinical follow-up. The aim of this study was to
compare the reliability, clinical accuracy, quality, usefulness, and readability of
ChatGPT-5.2, Gemini 3, and DeepSeek V3.2 responses to patient questions
regarding geriatric rehabilitation.
Materials and Method: In this cross-sectional comparative content analysis,
24 predefined questions on geriatric rehabilitation were developed from
YouTube comments, relevant literature, and clinical experience. Each question
was submitted to three large language models under standardized conditions.
Anonymized responses were independently evaluated by two experienced
physiotherapists for reliability, clinical accuracy, quality, and usefulness, with
disagreements resolved by consensus with a specialist physician. Readability
was assessed using the Flesch Reading Ease scores.
Results: No statistically significant difference was found among the three
large language models in terms of reliability scores (p = 0.097). However,
significant differences were observed for clinical accuracy, quality, usefulness,
readability, and text characteristics (all p = 0.001). ChatGPT-5.2 and DeepSeek
V3.2 showed the highest clinical accuracy scores, while ChatGPT-5.2 was
superior in terms of quality and usefulness. For readability, ChatGPT-5.2 and
Gemini 3 outperformed DeepSeek V3.2.
Conclusion: Although all three large language models generally produced
reliable content, ChatGPT-5.2 and DeepSeek V3.2 showed stronger clinical
accuracy performance. Nevertheless, because of the risk of incorrect information
being generated, the use of large language models by the older population
should preferably be done under expert supervision.
Keywords: Geriatrics; Rehabilitation; Artificial Intelligence; Patient
Education as Topic; Health Literacy.
While ChatGPT provided generally acceptable clinical information, variability in readability and expert ratings raises patient safety concerns and AI-generated health content should be reviewed and tailored to older adults' health literacy needs before clinical use.
Merve Arı, N. Ilçin, Hatice Yağcıoğlu et al.· Revista da Associação Médica...· 0 citations
MIND was able to answer many questions about escitalopram in a manner deemed accurate, complete, and safe by psychiatrists the majority of the time, and represents a step towards building safe LLM systems to enhance patient education in psychiatry.
Alexander J. Hish, A. Nagendran, S. Compton· 0 citations
Under the tested single-query and five-sentence conditions, DeepSeek-R1 achieved higher expert-rated clinical accuracy and Chinese-guideline concordance, and caregivers should use chatbot information only as a supplementary resource.
Fang-Yuan Wang, Xian-Hong Li, Ying-Hui Guan et al.· Frontiers in Pediatrics· 0 citations
There may be significant differences in how effectively LLMs support patients with surgical queries, particularly in areas needing detailed explanation, and usually required minimal clarification in areas needing detailed explanation.
T. Davis, B. Guevel, K. Logishetty et al.· Annals of the Royal College...· 0 citations
LLMs can support communication and education following spine surgery when used with structured prompting when used with structured prompting and ChatGPT and Claude showed the highest correctness and completeness, particularly for practitioner-directed answers.
S. Wegmann, T. Rosenkranz, Philipp Egenolf et al.· European spine journal· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.