Application of Artificial intelligence in COVID-19-related geriatric care: A scoping review

Emina Burnazovic; Amanda Yee; Joshua Levy; Genevieve Gore; Samira Abbasgholizadeh Rahimi

doi:10.1016/j.archger.2023.105129

Application of Artificial intelligence in COVID-19-related geriatric care: A scoping review

Arch Gerontol Geriatr. 2024 Jan:116:105129. doi: 10.1016/j.archger.2023.105129. Epub 2023 Jul 22.

Authors

Emina Burnazovic¹, Amanda Yee², Joshua Levy³, Genevieve Gore⁴, Samira Abbasgholizadeh Rahimi⁵

Affiliations

¹ Integrated Biomedical Engineering and Health Sciences, Department of Computing and Software, Faculty of Engineering, McMaster University, Hamilton, ON, Canada.
² Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.
³ Department of Pharmacology and Therapeutics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.
⁴ Schulich Library of Physical Sciences, Life Sciences and Engineering, McGill University, Montreal, QC, Canada.
⁵ Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada; Mila-Quebec Artificial Intelligence Institute, Montreal, QC, Canada; Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada. Electronic address: samira.rahimi@mcgill.ca.

PMID: 37542917
DOI: 10.1016/j.archger.2023.105129

Abstract

Background: 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.

Method: 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.

Results: 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.

Discussion: 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.

Keywords: Artificial intelligence; COVID-19; Geriatrics; Machine learning; Older adults; Scoping review.

Publication types

Research Support, Non-U.S. Gov't
Scoping Review

MeSH terms

Aged
Artificial Intelligence*
COVID-19*
Consensus
Databases, Factual
Female
Humans
Pandemics