ArticleRisk management and healthcare policy2025
Evaluation of Three Large Language Models' Response Performances to Inquiries Regarding Post-Abortion Care in the Context of Chinese Language: A Comparative Analysis.
Article in Risk management and healthcare policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
- Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review.Journal of medical Internet research · 2026Article
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2 authors.
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Abstract
Background: This study aimed to evaluate the response performances of three large language models (LLMs) (ChatGPT, Kimi, and Ernie Bot) to inquiries regarding post-abortion care (PAC) in the context of the Chinese language. Methods: The data was collected in October 2024. Twenty questions concerning the necessity of contraception after induced abortion, the best time for contraception, choice of a contraceptive method, contraceptive effectiveness, and the potential impact of contraception on fertility were used in this study. Each question was asked three times in Chinese for each LLM. Three PAC consultants conducted the evaluations. A Likert scale was used to score the responses based on accuracy, relevance, completeness, clarity, and reliability. Results: The number of responses received "good" (a mean score > 4), "average" (3 < mean score ≤ 4), and "poor" (a mean score ≤ 3) in overall evaluation was 159 (88.30%), 19 (10.57%), and 2 (1.10%). No statistically significant differences were identified in the overall evaluation among the three LLMs ( Conclusion: The three LLMs performed well overall and showed great potential for application in PAC consultations. The accuracy of the LLMs' responses should be improved through continuous training and evaluation.
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