Bridging Artificial and Human Intelligence: Comparative Performance of AI Models and Neurologists in Stroke Recognition- A Cross-Sectional Study

Background Stroke is a major cause of disability and mortality worldwide. Thus, early detection and intervention, along with appropriate triage, are crucial. The development of large language models (LLMs), such as ChatGPT and Gemini, presents new potential for artificial intelligence in healthcare, including clinical decision support. The objective of this study was to evaluate the diagnostic accuracy and quality of triage recommendations from leading LLMs compared to those from board-certified neurologists in patients with suspected acute stroke. Methods This was a cross-sectional study of 200 posts in the Reddit “AskDocs” section related to possible symptoms of stroke. These posts elicited responses from two LLMs, ChatGPT-4 and Gemini, as well as two board-certified neurologists. Two experienced emergency medicine specialists, who were independent of the survey, evaluated responses for three criteria online using 7-point Likert scales: Ease of Understanding, Scientific Adequacy and Overall Satisfaction. The outcome of interest was advising an Emergency Department (ED) visit. Results Neurologists were much more willing to advocate for visiting the ED (58.5%) than ChatGPT (45%) or Gemini (45%) (p < 0.001). Second, AI models often gave “Unable to determine” response (ChatGPT: 11.5%, Gemini: 14.5%), which was not reported by the neurologists (0%). Regarding quality, neurologist responses were rated highest for Ease of Understanding (Median 7) and Overall Satisfaction (Median 7) (p < 0.001 for both comparisons). Notably, the ChatGPT’s adequacy was rated significantly higher (focus group: Median 6 vs. neurologists: Median 5, p < 0.01), although the levels of Gemini’s scientific adequacy did not differ from those of neurologists (Median 5 vs. 5, p = 0.14). Conclusion LLMs showed strong scientific adequacy and good clarity of presentation; however, their conservative triage strategy raises important patient safety concerns in time-sensitive neurological emergencies such as stroke.