Validity and reliability of ChatGPT's responses on dietary supplements in Japan: A quality assessment and content analysis

Abstract

Objective: This study evaluated the validity and reliability of large language model (LLM) responses on dietary supplements (DS), a domain marked by scientific controversy and misinformation. The goal was to support informed consumer decisions and guide improvements in LLM performance. Methods: We collected responses from GPT-4 and GPT-4o on the effects of 30 DS on six diseases. Two medical professionals categorized each response as "Effective," "Uncertain," or "Not Effective." They also created a guideline to assess evidence-based effectiveness and compared it with LLM-generated responses to determine accuracy. Additionally, we conducted qualitative content analysis to identify response patterns and misleading content. Results: GPT-4 and GPT-4o affirmed DS effectiveness in only 10% of cases, with 40% rated as "Uncertain" and 50% as "Not Effective." Accuracy was about 57%, considerably lower than that observed in nutrition-related studies (57% in DS vs. 80% ∼ in structured nutrition tasks"). Content analysis showed templated responses, frequent ambiguity, and occasional inclusion of irrelevant or incorrect information. Conclusion: Our findings suggest that ChatGPT's responses on dietary supplements are generally cautious but often ambiguous, with a moderate risk of misinformation. As generative AI becomes a common source for health advice, these limitations could mislead users. Enhancing LLMs' evidence-based accuracy and ensuring consistent professional guidance are essential. Innovation: This is the first study to assess the validity and reliability of LLM-generated responses on dietary supplements using both quantitative and qualitative methods. We also developed a novel evidence-based framework to evaluate supplement effectiveness, providing a new tool for future research and supporting safer AI-assisted health communication.

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