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

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 &#...

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Veröffentlicht in:International journal of production research Jg. 63; H. 2; S. 594 - 627
Hauptverfasser: Balasubramanian, Sreejith, Shukla, Vinaya, Islam, Nazrul, Upadhyay, Arvind, Duong, Linh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Taylor & Francis 17.01.2025
Taylor & Francis LLC
Schlagworte:
Algorithms
Artificial intelligence
Computer vision
COVID-19
Health care
Health care industry
healthcare
Industry 4.0
Machine learning
Pandemics
Stakeholders
UAE
ISSN:0020-7543, 1366-588X
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Zusammenfassung: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.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2023.2263102