Algorithms on the rise: a machine learning-driven survey of prostate cancer literature.

ABSTRACT:

Introduction

Machine learning (ML) has shown significant potential in improving prostate cancer (PCa) diagnosis, prognosis, and treatment planning. Despite rapid advancements, a comprehensive quantitative synthesis of global research trends and the knowledge structure of ML applications in PCa remains lacking. This study aimed to systematically map the evolution, research hotspots, and collaborative landscape of ML-PCa research.

Methods

A systematic bibliometric review was performed on English-language articles and reviews published between January 2005 and December 2024. Publications were retrieved from the Web of Science (WOS) and Scopus databases. Analytical tools including CiteSpace, VOSviewer, and the R-bibliometrix package were employed to assess publication growth tre

SUBMITTER: Gu S 

PROVIDER: S-EPMC12527887 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature