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Artificial Intelligence in Health

REVIEW ARTICLE
A bibliometric analysis of using machine

learning and artificial intelligence in prostate
cancer detection

2
Syed Asif Raza *, Nadeem Pervez , Ikram A. Burney , and Momena Ahmed 3
1
2
1 Department of Marketing and Business Analytics, Texas A&M University-Commerce, Texas, USA
2 Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat, Oman
3 Department of Biology and Biochemistry, University of Houston, Houston, Texas, USA

Abstract

Prostate cancer stands as one of the most prevalent cancers globally among men,
exhibiting substantial geographical variations in both incidence and mortality.
While developed countries bear a higher incidence, developing countries grapple
with elevated mortality rates. The heightened mortality in the latter is attributed to
variations in practices that impede early diagnosis. In this context, the integration
of artificial intelligence (AI) and machine learning (ML) has become increasingly
common to improve the diagnostic accuracy of prostate cancer. This review
delves into the existing literature to scrutinize the utilization of AI and ML in the
diagnosis of prostate cancer. To compile relevant literature, comprehensive searches
*Corresponding author: were conducted on research databases, including SCOPUS, Web of Science, and
Syed Asif Raza Google Scholar, to identify articles related to AI or ML (AI/ML) in the diagnosis and
(syed.raza@tamuc.edu)
management of prostate cancer. Using a screening criterion, 293 reviewed research
Citation: Raza SA, Pervez N, papers were identified. The two most consistent themes were predictive modeling
Burney IA, et al., 2024, A and the application of AI/ML tools for cancer grading and radiomics. AI and ML
bibliometric analysis of using
machine learning and artificial enhance diagnostic accuracy by reducing inter-individual variation in Gleason’s
intelligence in prostate cancer scoring and complimenting the interpretation of multiparametric magnetic
detection. Artif Intell Health, resonance imaging (mpMRI). A few publications reported the use of AI/ML tools
1(1): 3-15.
https://doi.org/10.36922/aih.1958 that combine histopathology with MRI signals. The literature surveyed indicates a
compelling potential for AI and ML to improve diagnostic accuracy in prostate cancer.
Received: September 30, 2023
Emerging literature suggests the use of a combination of demographic features,
Accepted: December 23, 2023 clinical data, serological markers, pathological grading and radiological factors, and
Published Online: December 26, genomic data to propose an accurate, non-invasive diagnosis of clinically significant
2023 prostate cancer.
Copyright: © 2024 Author(s).
This is an Open-Access article
distributed under the terms of the Keywords: Prostrate cancer; Biopsy; Machine learning; Artificial intelligence; Bibliometrics
Creative Commons Attribution analysis, Network and content analysis; Magnetic resonance imaging; Gleason score
License, permitting distribution,
and reproduction in any medium,
provided the original work is
properly cited.
1. Introduction
Publisher’s Note: AccScience
Publishing remains neutral with Prostate cancer stands as the most prevalent cancer among men globally, with more
regard to jurisdictional claims in [1,2]
published maps and institutional than 1.4 million cases diagnosed annually . However, a notable geographic variation
affiliations. in incidence rates exists [1,3] . In developed countries, the incidence rates are nearly

Volume 1 Issue 1 (2024) 3 https://doi.org/10.36922/aih.1958