Carpal tunnel syndrome: Do large language models utilize the clinical practice guidelines?

Background: Carpal tunnel syndrome (CTS) is a common entrapment neuropathy, and as patients increasingly seek health information online, the role of artificial intelligence (AI) in providing accurate medical advice is gaining attention. This study evaluates the responses of two large language models (LLMs), ChatGPT and Google Gemini, regarding CTS management, comparing their outputs to established clinical practice guidelines in terms of accuracy, completeness, and readability. Methods: Seven questions related to CTS treatment were developed and posed to both ChatGPT and Google Gemini. The generated responses were assessed by eight fellowship-trained hand surgeons, who independently classified each answer as “appropriate,” “appropriate but incomplete,” or “inappropriate.” Readability was determined as grade level using a standardized online calculator that assessed multiple readability formulas. Results: Out of 112 total responses evaluated, 76 were deemed appropriate, with ChatGPT providing 46 appropriate responses compared with 30 from Gemini. Readability analysis indicated that ChatGPT’s responses averaged at a 12th-grade reading level, while Gemini’s averaged a 10th-grade level. Notably, 24 responses were graded as “appropriate but incomplete,” emphasizing the need for comprehensive information. Conclusion: This study demonstrates the potential of AI-driven platforms in enhancing patient education on CTS management. ChatGPT outperformed Gemini in accuracy, but both models showed limitations in completeness. The findings highlight the importance of ensuring that AI-generated health information is not only accurate but also accessible to patient populations. Future research should focus on improving the integration of AI with health care provider insights and quality resources to enhance patient outcomes and understanding.