ORIGINAL RESEARCH article
Front. Public Health
Sec. Digital Public Health
This article is part of the Research TopicGenerative AI and Large Language Models in Medicine: Applications, Challenges, and OpportunitiesView all 9 articles
Development of a Questionnaire for Assessing the Use of ChatGPT in Primary and Secondary Disease Prevention
Provisionally accepted
- 1Semmelweis University, Budapest, Hungary
- 2Semmelweis Egyetem Doktori Iskolak, Budapest, Hungary
- 3Semmelweis Egyetem, Budapest, Hungary
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Background Many individuals seek health-related guidance through ChatGPT OpenAI (San Francisco, CA, USA), due to its convenience and perceived reliability, often in place of, or as a supplement to, professional medical advice. This raises concerns about the accuracy of information provided and the potential for misinterpretation. On the other hand, ChatGPT offers a promising avenue for complementing traditional health prevention processes. Aims This study aimed to develop and validate self-completion questionnaire among adults that evaluates the use of role of ChatGPT in primary and secondary health prevention, to explore the extent to which users utilize ChatGPT for disease prevention and health maintenance. Method Questionnaire items were derived from a systematic literature review and comprised demographics, internet‐use metrics, and validated items from the Brief Health Literacy Screening Tool. ChatGPT usage was structured into three domains: knowledge, attitudes, and behaviours. Test–retest reliability was quantified by Kendall's tau, and internal consistency by Cronbach's Alpha. Results During the validation phase, the questionnaire was administered to a sample of 22 participants (16 female, 6 male), each of whom completed it twice, resulting in a total of 44 responses. Knowledge items demonstrated significant test–retest stability (Kendall's τ, p < 0.01). For behaviour items, seven achieved perfect reliability (τ = 1.00), and five exceeded τ > 0.70. Attitude items similarly showed high stability, with three at τ = 1.00 and three above τ > 0.70. Internal consistency was acceptable (raw Cronbach's α = 0.771). Discussion Our reliability analysis demonstrated that the items of the instrument exhibit good internal consistency, with Cronbach's Alpha values exceeding the commonly accepted threshold for exploratory research. Moreover, the questionnaire's design is inherently model-independent, allowing for its straightforward adaptation to assess user interactions with a variety of conversational artificial intelligence systems beyond ChatGPT. Conclusion In conclusion, this study presents an initially validated questionnaire that captures how individuals employ ChatGPT for both primary and secondary disease prevention. The tool addresses key dimensions of artificial intelligence use and enables meaningful comparisons across populations with different social and educational backgrounds. Keywords ChatGPT, a Large Language Model, Disease Prevention, Self-completion Questionnaire Development, Validation
Keywords: ChatGPT, a Large Language Model, disease prevention, Self-completion Questionnaire Development, Validation
Received: 20 Sep 2025; Accepted: 05 Dec 2025.
Copyright: © 2025 Angyal, Bertalan, Domján, Feith and Dinya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Viola Angyal
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