Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic

Sreejith Balasubramanian, Vinaya Shukla, Nazrul Islam, Arvind Upadhyay and Linh Duong

International Journal of Production Research, 2025, vol. 63, issue 2, 594-627

Abstract: The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like Artificial Intelligence (AI). However, previous research on the topic has been limited and fragmented, leading to an incomplete understanding of the ‘what’, ‘where’ and ‘how’ of its application, as well as its associated benefits and challenges. This study proposes a comprehensive AI framework for healthcare and assesses its effectiveness within the UAE's healthcare sector. It provides valuable insights into AI applications for healthcare stakeholders that range from the molecular to the population level. The study covers the different computational techniques employed, from machine learning to computer vision, and the various types of data inputs fed into these techniques, including clinical, epidemiological, locational, behavioural and genomic data. Additionally, the research highlights AI's capacity to enhance healthcare's operational, quality-related and social outcomes, and recognises regulatory policies, technological infrastructure, stakeholder cooperation and innovation readiness as key facilitators of AI adoption. Lastly, we stress the importance of addressing challenges such as data privacy, security, generalisability and algorithmic bias. Our findings are relevant beyond the pandemic in facilitating the development of AI-related policy interventions and support mechanisms for building resilient healthcare sector that can withstand future challenges.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2023.2263102 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:63:y:2025:i:2:p:594-627

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2023.2263102

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().