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

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: Artificial intelligence; Clinical domain; Deep learning; Machine learning; Smart literature review.

Fig. 1

Data flow process between clinical domain and AI system (researcher’s construction)

Fig. 2

Smart literature review. Source: Adapted from Page et al., (2021)

Fig. 3

ML/DL models utilized in past studies (researcher’s construction)

Fig. 4

AI publication in the clinical domain (researcher’s construction)

Fig. 5

Diseases investigation using ML/DL model in clinical (researcher’s construction)

Fig. 6

Opted ML/DL model as a result in the clinical domain (researcher’s construction)