Veras Mirella, Pardo Jordi, Lê Mê-Linh, Jussup Cindy, Tatmatsu-Rocha José Carlos, Welch Vivian
Department of Physical Therapy, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada.
Centre on Aging, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
J Pers Med. 2025 Jan 14;15(1):29. doi: 10.3390/jpm15010029.
: Artificial intelligence (AI) is transforming healthcare by enhancing diagnostic accuracy, treatment, and patient monitoring, benefiting older adults by offering personalized care plans. AI-powered tools help manage chronic conditions and maintain independence, making them a valuable asset in addressing aging challenges. : The objectives are as follows: 1. To identify and describe AI-power-based exercise programs for older adults. 2. To highlight primary evidence gaps in AI interventions for functional improvement and mobility. 3. To evaluate the quality of existing reviews on this topic. : The evidence gap map (EGM) will follow the five-step method, adhering to the Campbell Collaboration guidelines and, if available at the time of reporting, PRISMA-AI standards. Guided by the Metaverse Equitable Rehabilitation Therapy framework, this study will categorize findings across domains like equity, health service integration, interoperability, governance, and humanization. The study will include systematic reviews, randomized controlled trials, and pre-and post-intervention designs. will be reported following PRISMA-AI guidelines. We will use AMSTAR-2 Checklist for Analyzing Systematic Reviews on AI Interventions for Improving mobility and function in Older Adults to evaluate the reliability of systematic reviews and focus on internal validity. : This comprehensive analysis will act as a critical resource for guiding future research, refining clinical interventions, and influencing policy decisions to enhance AI-driven solutions for aging populations. The EGM aims to bridge existing evidence gaps, fostering a more informed, equitable, and effective approach to AI solutions for older adults.
人工智能(AI)正在通过提高诊断准确性、治疗和患者监测来改变医疗保健,通过提供个性化护理计划使老年人受益。人工智能驱动的工具有助于管理慢性病并维持独立性,使其成为应对老龄化挑战的宝贵资产。
证据差距图(EGM)将遵循五步方法,遵循坎贝尔协作指南,并在报告时若有可用的情况下遵循PRISMA-AI标准。在元宇宙公平康复治疗框架的指导下,本研究将对公平性、卫生服务整合、互操作性、治理和人性化等领域的研究结果进行分类。该研究将包括系统综述、随机对照试验以及干预前后设计。将按照PRISMA-AI指南进行报告。我们将使用AMSTAR-2清单来分析关于改善老年人移动性和功能的人工智能干预的系统综述,以评估系统综述的可靠性并关注内部有效性。
这种全面分析将成为指导未来研究、完善临床干预措施以及影响政策决策的关键资源,以增强针对老龄化人口的人工智能驱动解决方案。证据差距图旨在弥合现有证据空白,促进对老年人人工智能解决方案采取更明智、公平和有效的方法。