基于糖尿病足多阶段病程管理的AI智能体构建与验证OA

Objective To investigate the application value of a tool-driven artificial intelligence(AI)agent in clinical decision-making across multiple stages of the disease course in patients with diabetic foot.Methods Using the large language model(LLM)Qwen3-max as the reasoning engine,an AI agent adaptable to repeated follow-up scenarios in diabetic foot was constructed by integrating the ReAct framework,retrieval-augmented generation(RAG)technology,and multimodal data-processing tools.Validation was performed based on retrospective data from 34 patients with diabetic foot(a total of 140 visits).Performance differences were compared among the AI agent,the native large language model Qwen3-max,and the medical LLM Baichuan-M1-14B.Results The clinical practicality score of the AI agent reached 8.29±0.91,significantly higher than that of the Qwen3-max(7.56±0.70,t=4.19,P＜0.001)and Baichuan-M1-14B(7.82±0.67,t=3.67,P＜0.001).Moreover,the advantage became more pronounced as the number of disease-course visits increased.In the high disease-course group(≥7 visits),the AI agent scored 9.50±0.58,exceeding Qwen3-max(7.50±0.58)and Baichuan-M1-14B(8.50±0.58)by 2.00 and 1.60 points,respectively.The AI agent increased the accuracy of diagnosis and staging to 94.1%through RAG technology,reduced the hallucination incidence to 8.7%via the ReAct framework,achieved an automatic recognition accuracy of 95.7%for key indicators,and shortened the time required for disease-course data integration by 88.3%compared with traditional manual methods.Conclusions The AI agent constructed in this study achieved automated and standardized analysis and management of multiple disease-course episodes in diabetic foot,effectively reducing the"hallucination"risk of native LLMs,and can provide solid technical support for early warning,condition monitoring,and individualized treatment of diabetic foot.