Arkangel AI, OpenEvidence, ChatGPT, Medisearch: Are They Objectively up to Medical Standards? A Real-Life Assessment...

To compare the performance of 4 large language model chatbots in response time and quality of clinical answers, evaluated by specialists using predefined validity criteria.

Between June 1 and September 20, 2025, four clinical vignettes (orthopedics, pediatrics, gynecology, and psychiatry) were developed by independent experts and answered by 4 conversational agents: Arkangel AI, OpenEvidence, ChatGPT, and Medisearch. Each vignette included 4 questions (diagnosis, clinical management, research, and general knowledge). Responses were independently evaluated by external clinicians using an 8-criterion Likert scale assessing correctness, consensus agreement, absence of bias, adherence to standards of care, timeliness, patient safety, authenticity of cited references, and contextual appropriateness. Response times were summarized using medians and interquartile ranges.

A total of 128 question-answer pairs (1024 evaluations) were analyzed. Overall satisfaction ranged from 71.1% (727) to 93% (952) across agents, with statistically significant differences (Kruskal-Wallis P<.001). Dissatisfaction was most frequent for reference authenticity in some ChatGPT modes (75% [n 91/128]-97% [n 119/128]), whereas Arkangel AI-Deep, ChatGPT-Deep, and OpenEvidence showed 100% satisfaction for this criterion. Satisfaction was high for correctness and consensus, with greater variability for bias and patient safety. Satisfaction differed by specialty, with higher scores in gynecology and lower scores in pediatrics (P<.001). Median response times ranged from 18 seconds to 12.8 minutes, with significant differences across agents and modes (Wilcoxon P<.05).

Large language model chatbots showed substantial variability across validity dimensions when assessed using expert clinical judgment, supporting the need for standardized, multidimensional evaluation frameworks.