摘要
川崎病的临床管理面临早期诊断难、个体化治疗不足、信息获取滞后及多学科协作低效等挑战。该文探讨了人工智能大模型DeepSeek在川崎病管理中的应用:(1)基于多模态数据(影像、实验室及临床数据)融合分析,提升早期诊断准确性;(2)动态调整治疗方案,实现个体化精准医疗;(3)实时获取并整合全球最新诊疗指南与研究成果,优化诊疗流程;(4)提供个性化健康宣教内容,提升家长参与度;(5)构建诊疗数据共享平台,支持智能决策与多学科协作。
Clinical management of Kawasaki disease faces several challenges,including difficulties in early diagnosis,insufficient personalized treatment,delayed access to information,and inefficient multidisciplinary collaboration.This paper explores the application of the DeepSeek AI model in the management of Kawasaki disease:(1)Enhancing early diagnosis accuracy through the integration and analysis of multimodal data(imaging,laboratory,and clinical data);(2)Dynamically adjusting treatment plans to achieve personalized medicine;(3)Integrating the latest global guidelines and research findings in real-time to optimize clinical processes;(4)Providing personalized health education content to enhance parental involvement;(5)Establishing a platform for sharing clinical data to support intelligent decision-making and multidisciplinary collaboration.
作者
潘炎
焦富勇
PAN Yan;JIAO Fu-Yong(Children's Hospital of Shaanxi Provincial People's Hospital/Diagnosis and Treatment Center of Kawasaki Disease of Shaanxi Province,Xi'an 710068,China;不详)
出处
《中国当代儿科杂志》
北大核心
2025年第5期524-528,共5页
Chinese Journal of Contemporary Pediatrics
基金
2025年湖北省积极健康研究院健康科学研究孵化项目(HAHRI2025-F022)。
