Burnazovic Emina, Yee Amanda, Levy Joshua, Gore Genevieve, Abbasgholizadeh Rahimi Samira
Integrated Biomedical Engineering and Health Sciences, Department of Computing and Software, Faculty of Engineering, McMaster University, Hamilton, ON, Canada.
Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.
Arch Gerontol Geriatr. 2024 Jan;116:105129. doi: 10.1016/j.archger.2023.105129. Epub 2023 Jul 22.
Older adults have been disproportionately affected by the COVID-19 pandemic. This scoping review aimed to summarize the current evidence of artificial intelligence (AI) use in the screening/monitoring, diagnosis, and/or treatment of COVID-19 among older adults.
The review followed the Joanna Briggs Institute and Arksey and O'Malley frameworks. An information specialist performed a comprehensive search from the date of inception until May 2021, in six bibliographic databases. The selected studies considered all populations, and all AI interventions that had been used in COVID-19-related geriatric care. We focused on patient, healthcare provider, and healthcare system-related outcomes. The studies were restricted to peer-reviewed English publications. Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought.
Six databases were searched , yielding 3,228 articles, of which 10 were included. The majority of articles used a single AI model to assess the association between patients' comorbidities and COVID-19 outcomes. Articles were mainly conducted in high-income countries, with limited representation of females in study participants, and insufficient reporting of participants' race and ethnicity.
This review highlighted how the COVID-19 pandemic has accelerated the application of AI to protect older populations, with most interventions in the pilot testing stage. Further work is required to measure effectiveness of these technologies in a larger scale, use more representative datasets for training of AI models, and expand AI applications to low-income countries.
老年人受新冠疫情的影响尤为严重。本综述旨在总结目前人工智能（AI）在老年人新冠病毒病（COVID-19）筛查/监测、诊断和/或治疗中应用的证据。
本综述遵循乔安娜·布里格斯研究所以及阿克西和奥马利的框架。一名信息专家从数据库建立之日起至2021年5月，在六个文献数据库中进行了全面检索。入选的研究考虑了所有人群以及在COVID-19相关老年护理中使用的所有AI干预措施。我们重点关注与患者、医疗服务提供者和医疗系统相关的结果。研究仅限于经同行评审的英文出版物。两名作者独立筛选已识别记录的标题和摘要，阅读选定的全文，并使用经过验证的数据提取表从纳入的研究中提取数据。分歧通过协商一致解决，如果无法达成一致，则征求第三位评审员的意见。
检索了六个数据库，共获得3228篇文章，其中10篇被纳入。大多数文章使用单一AI模型评估患者合并症与COVID-19结局之间的关联。文章主要在高收入国家开展，研究参与者中女性代表性有限，且对参与者的种族和民族报告不足。
本综述强调了COVID-19疫情如何加速了AI在保护老年人群方面的应用，大多数干预措施处于试点测试阶段。需要进一步开展工作，以更大规模衡量这些技术的有效性，使用更具代表性的数据集来训练AI模型，并将AI应用扩展到低收入国家。
