ArticleDigital health
Can large language models respond health education questions for patients with palmar hyperhidrosis? A comparative study of ChatGPT and DeepSeek.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Objective: To compare the adaptability of two large language models: ChatGPT and DeepSeek in responding to health education questions related to patients with palmar hyperhidrosis. Methods: Based on clinical guidelines and expert experience, 17 health education questions relevant to palmar hyperhidrosis were developed and posed separately to ChatGPT and DeepSeek. Twelve experienced thoracic surgery experts independently evaluated the adaptability of the responses generated by both models. Each response was rated using a five-point Likert scale to quantitatively analyze the adaptability of the information provided. Results: Both language models demonstrated good adaptability in addressing health education questions related to palmar hyperhidrosis. In the English context, 10 responses of ChatGPT received a full score (5 points) from more than 50% of experts, while DeepSeek did so for 8. In the Chinese context, both ChatGPT and DeepSeek receive 10 responses a full score (5 points) from more than 50% of experts. ChatGPT outperformed DeepSeek in the English-language setting, whereas DeepSeek showed superior overall performance in the Chinese context. Conclusion: This preliminary study demonstrates that both ChatGPT and DeepSeek are capable of effectively addressing health education questions for patients with palmar hyperhidrosis. ChatGPT performs better in English-language setting, while DeepSeek shows greater adaptability in Chinese-language context. However, human review remains essential to ensure the accuracy and reliability of the provided information in practical applications.
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