Extending artificial intelligence research in the clinical domain: a theoretical perspective

Renu Sabharwal (), Shah J. Miah () and Samuel Fosso Wamba ()
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Renu Sabharwal: The University of Newcastle
Shah J. Miah: The University of Newcastle
Samuel Fosso Wamba: TBS Business School

Annals of Operations Research, 2025, vol. 348, issue 3, No 19, 1713-1744

Abstract: Abstract Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.

Keywords: Machine learning; Artificial intelligence; Clinical domain; Smart literature review; Deep learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-022-05035-1

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