Performance of Large Language Model-Based Chatbots in Primary Health Care Teleconsultations: A Comparison Between Human and Artificial Intelligence-Generated Responses.

INTRODUCTION: Telehealth is a strategic component of primary health care and has advanced in Brazil through the National Telehealth Program. Its benefits can be enhanced by artificial intelligence (AI), which has emerged as a promising tool. This study aims to compare the performance of real human and AI-generated responses to queries submitted to the teleconsultation services of the Telehealth Center of the UFMG Faculty of Medicine (NUTEL FM-UFMG), a member of the Telehealth Brazil Program. METHODS: This is a comparative cross-sectional study of 180 real human and AI-generated responses, evaluated in a blinded manner according to quality criteria (medical adequacy, conciseness, coherence, and comprehensibility), risk potential, authorship identification accuracy, and inquiry resolution. Data from NUTEL FM-UFMG (January 2020 to May 2024) were utilized, covering cardiology, endocrinology, and obstetrics/gynecology (OB-GYN). Statistical analysis included the Shapiro-Wilk test, Kruskal-Wallis test, Nemenyi multiple comparison test, chi-square test, and Fisher's exact test. RESULTS: Across all specialties, a significant difference was observed in comprehensibility, with AI mean scores surpassing those of humans. For the remaining quality criteria, as well as for risk potential and inquiry resolution, no significant differences were found, despite AI scoring higher than humans. Within specific specialties, significant differences was observed in endocrinology (except conciseness) and cardiology (in conciseness); AI showed superior means. Across all specialties, as well as individually within endocrinology and OB-GYN, the accuracy of authorship identification (human vs. AI) was statistically significant. CONCLUSION: Despite existing limitations, AI demonstrates substantial potential as a support tool for teleconsultation services.