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人工智能在增龄相关衰弱患者康复中的应用专家共识

黄丽萍 邹永向 费燕琼 侯景明 黄瑞 李伟 刘晓旻 庞日朝 王志永 于宁波 张弼 江山 张立新 曹磊 周明 钱春生 张攻孜 徐佳宁

康复学报2026,Vol.36Issue(7):457-472,16.
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康复学报2026,Vol.36Issue(7):457-472,16.DOI:10.3724/SP.J.1329.2026.07001

人工智能在增龄相关衰弱患者康复中的应用专家共识

Expert Consensus on the Application of Artificial Intelligence in Rehabilitation Management for Age-Related Frailty Patients

黄丽萍 1邹永向 2费燕琼 3侯景明 4黄瑞 5李伟 6刘晓旻 7庞日朝 8王志永 9于宁波 10张弼 11江山 12张立新 13曹磊 14周明 1钱春生 15张攻孜 1徐佳宁16

作者信息

  • 1. 中国人民解放军总医院第一医学中心,北京 100853
  • 2. 中国科学院自动化研究所,北京 100190
  • 3. 上海交通大学,上海 200240
  • 4. 中国人民解放军陆军军医大学第一附属医院,重庆 400038
  • 5. 电子科技大学,四川 成都 611731
  • 6. 山东大学,山东 济南 250100
  • 7. 郑州大学,河南 郑州 450001
  • 8. 西部战区总医院,四川 成都 610083
  • 9. 哈尔滨工业大学(深圳),广东 深圳 518055
  • 10. 南开大学,天津 300071
  • 11. 中国科学院沈阳自动化研究所,辽宁 沈阳 110016
  • 12. 中日友好医院,北京 100029
  • 13. 中国医科大学附属盛京医院,辽宁 沈阳 110004
  • 14. 首都医科大学宣武医院,北京 100053
  • 15. 中国人民解放军联勤保障部队第九六二医院,黑龙江 哈尔滨 150086
  • 16. 中国人民解放军总医院第四医学中心,北京 100048
  • 折叠

摘要

Abstract

With the acceleration of population aging,age-related frailty,a clinical syndrome characterized by the progressive decline of physiological functional reserves across multiple organ systems with advancing age,has become a key public health con-cern affecting health outcomes and quality of life in older adults.This condition manifests across four dimensions:physical,cogni-tive,psychological,and social.Traditional assessment methods demonstrate significant limitations in dynamic monitoring and per-sonalized interventions.Artificial intelligence(AI)and intelligent rehabilitation technologies,which feature strengths including cross-modal data fusion,dynamic monitoring,and individualized predictive modeling,provide innovative approaches for the precise management of age-related multidimensional frailty.This consensus document was developed by an interdisciplinary expert panel following evidence-based methodological principles.Based on systematic literature review and the grading criteria of the Oxford Centre for Evidence-Based Medicine(OCEBM),this study systematically reviews current evidence and puts forward standardized recommendations for AI applications in the assessment and intervention of age-related multidimensional frailty.The content covers the technical framework of AI across perception,data processing,model integration,and intervention guidance.It proposes typical AI-assisted assessment and intervention pathways across the physical,cognitive,psychological,and social dimensions of frailty,and puts forward clinical application recommendations for various technologies,including exoskeleton robots,virtual reality(VR)com-bined with brain-computer interfaces(BCI),digital psychological intervention platforms,and social companion robots,with corre-sponding evidence-based recommendation grades.Furthermore,the consensus clarifies the indications and contraindications for AI applications in the rehabilitation of age-related multidimensional frailty,emphasizing the importance of multicenter clinical valida-tion,ethical compliance,data security,and interdisciplinary collaboration.In the future,AI technology will evolve towards digital twin systems,personalized rehabilitation algorithms,and multimodal deep learning platforms,promoting home-based,lightweight,and integrated development of rehabilitation devices,and constructing a new ecosystem of full-cycle,multi-dimensional human-ma-chine collaborative rehabilitation.This consensus aims to provide practical guidance for clinicians,rehabilitation specialists,and other relevant practitioners,so as to promote the scientific,standardized,and sustainable application of AI technology in the rehabilitation of patients with age-related frailty.

关键词

增龄相关衰弱/人工智能/多维康复/智能评估/个体化干预/专家共识

Key words

age-related frailty/artificial intelligence/multidimensional rehabilitation/intelligent assessment/individualized intervention/expert consensus

引用本文复制引用

黄丽萍,邹永向,费燕琼,侯景明,黄瑞,李伟,刘晓旻,庞日朝,王志永,于宁波,张弼,江山,张立新,曹磊,周明,钱春生,张攻孜,徐佳宁..人工智能在增龄相关衰弱患者康复中的应用专家共识[J].康复学报,2026,36(7):457-472,16.

基金项目

国家重点研发计划项目(2022YFB4703205) (2022YFB4703205)

首都卫生发展科研专项项目(2026-2-5023) (2026-2-5023)

康复学报

2096-0328

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