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

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

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.

Citation

Balasubramanian, S., Shukla, V., Islam, N., Upadhyay, A., & Duong, L. (in press). Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic. International Journal of Production Research, https://doi.org/10.1080/00207543.2023.2263102

• Journal Article Type: Article
• Acceptance Date: Sep 17, 2023
• Online Publication Date: Oct 3, 2023
• Deposit Date: Oct 4, 2023
• Publicly Available Date: Oct 4, 2023
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Electronic ISSN: 1366-588X
• Publisher: Taylor & Francis
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1080/00207543.2023.2263102
• Keywords: Industrial and Manufacturing Engineering; Management Science and Operations Research; Strategy and Management
• Public URL: https://uwe-repository.worktribe.com/output/11151268
• Additional Information: Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tprs20; Received: 2023-01-09; Accepted: 2023-09-17; Published: 2023-10-03