Imran Rangraze, Khan Shehla Shafi
Department of Internal Medicine, RAKMHSU, Ras Al Khaimah, UAE.
Department of Psychiatry, RAKMHSU, Ras Al Khaimah, UAE.
BMC Geriatr. 2025 Apr 11;25(1):248. doi: 10.1186/s12877-025-05878-w.
To carry out systematic analysis of existing literature on role of Artificial Intelligence in geriatric patient healthcare.
A detailed online search was carried out using search phrases in reliable sources of information like Pubmed database, Embase database, Ovid database, Global Health database, PsycINFO, and Web of Science. Study specific information was gathered, including the organisation, year of publication, nation, setting, design of the research, information about population, size of study sample, group dynamics, eligibility and exclusion requirements, information about intervention, duration of exposure to the intervention , comparators, details of outcome measures, scheduling of evaluations, and consequences. After information gathering, the reviewers gathered to discuss any differences.
Thirty-one studies were finally selected for systemic review. Although there was some disagreement on the acceptance of AI-enhanced treatments in LTC settings, this review indicated that there was little consensus about the efficacy of those initiatives for older individuals. Social robots have been shown to increase social interaction and mood, but the data was more conflicting and less definitive for the other innovations and consequences. The majority of research evaluated a variety of results, which made it impossible to synthesise them in a meaningful way and prevented a meta-analysis. In addition, many studies have moderate to severe bias risks due to underpowered design CONCLUSION: It is challenging to determine whether AI supplemented technologies for geriatric patients are significantly beneficial. Although some encouraging findings were made, more study is required.
对关于人工智能在老年患者医疗保健中作用的现有文献进行系统分析。
使用诸如PubMed数据库、Embase数据库、Ovid数据库、全球健康数据库、PsycINFO和科学网等可靠信息源中的搜索短语进行详细的在线搜索。收集研究的具体信息，包括机构、出版年份、国家、研究背景、研究设计、人群信息、研究样本量、组间动态、纳入和排除标准、干预措施信息、干预暴露持续时间、对照、结果测量细节、评估安排以及结果。信息收集完成后，评审人员聚集在一起讨论分歧。
最终选择了31项研究进行系统评价。尽管在长期护理环境中对人工智能增强治疗的接受度存在一些分歧，但该评价表明，对于这些举措对老年人的疗效几乎没有共识。社交机器人已被证明能增加社交互动和改善情绪，但对于其他创新和结果的数据则更具冲突性且不太明确。大多数研究评估了多种结果，这使得无法以有意义的方式对其进行综合，也无法进行荟萃分析。此外，由于设计力度不足，许多研究存在中度至重度的偏倚风险。结论：确定人工智能辅助技术对老年患者是否具有显著益处具有挑战性。尽管有一些令人鼓舞的发现，但仍需要更多研究。
