Assessing online chat-based artificial intelligence models for weight loss recommendation appropriateness and bias in the presence of guideline incongruence

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Abstract
Background and aim Managing obesity requires a comprehensive approach that involves therapeutic lifestyle changes, medications, or metabolic surgery. Many patients seek health information from online sources and artificial intelligence models like ChatGPT, Google Gemini, and Microsoft Copilot before consulting health professionals. This study aims to evaluate the appropriateness of the responses of Google Gemini and Microsoft Copilot to questions on pharmacologic and surgical management of obesity and assess for bias in their responses to either the ADA or AACE guidelines. Methods Ten questions were compiled into a set and posed separately to the free editions of Google Gemini and Microsoft Copilot. Recommendations for the questions were extracted from the ADA and the AACE websites, and the responses were graded by reviewers for appropriateness, completeness, and bias to any of the guidelines. Results All responses from Microsoft Copilot and 8/10 (80%) responses from Google Gemini were appropriate. There were no inappropriate responses. Google Gemini refused to respond to two questions and insisted on consulting a physician. Microsoft Copilot (10/10; 100%) provided a higher proportion of complete responses than Google Gemini (5/10; 50%). Of the eight responses from Google Gemini, none were biased towards any of the guidelines, while two of the responses from Microsoft Copilot were biased. Conclusion The study highlights the role of Microsoft Copilot and Google Gemini in weight loss management. The differences in their responses may be attributed to the variation in the quality and scope of their training data and design.

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