Application effectiveness of large language models in health consultation for stroke patients

ObjectiveTo evaluate the efficacy and application potential of different large language models in health education consultation for stroke patients.MethodsUsing 21 common clinical questions of stroke and 2 sets of typical clinical scenario simulation cases,three rounds of tests were conducted on DeepSeek R1,ERNIE Bot X1,ChatGPT⁃4o,and Claude 3.7 Sonnet large language models.Three neurology department chief physicians were selected to evaluate the models from five dimensions:accuracy,comprehensiveness,understandability,humanistic care,and case analysis ability.ResultsThe overall performance of all models was good.DeepSeek R1 had the highest total score, followed by ChatGPT⁃4o,Claude 3.7 Sonnet, and ERNIE Bot X1.DeepSeek R1 and ChatGPT⁃4o performed outstandingly in complex clinical reasoning,evidence⁃based medical knowledge transmission,humanistic care,and case analysis.ERNIE Bot X1 had an advantage in the adaptability to the Chinese context.ConclusionsLarge language models showed positive auxiliary application value in the field of stroke health education.However,the clinical review mechanism needs to be combined to enhance the reliability of the information.The clinical application of these models could be promoted by optimizing the transparency of the algorithms and the adaptability to the local context.