Evaluating the performance of large language models in sarcopenia-related patient queries: a foundational assessment for patient-centered validation

Abstract

Despite minor differences in performance across domains, all three LLMs demonstrated acceptable accuracy and comprehensiveness when responding to sarcopenia-related queries. Their comparable results may reflect similarly recent training data and language capabilities. These findings suggest that LLMs could potentially serve as a valuable tool in patient education and care on sarcopenia. This study provides an initial, expert-based assessment of LLM information quality regarding sarcopenia. While the responses demonstrated good accuracy, this evaluation focuses on content correctness from a clinical perspective. Future research must complement these findings by directly engaging older adult cohorts before clinical implementation can be considered. However, human oversight remains essential to ensure safe and appropriate assessment and individually tailored advice and management.

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