Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.

ABSTRACT:

Background

This scoping review systematically maps externally validated machine learning (ML)-based models in cancer patient care, quantifying their performance, and clinical utility, and examining relationships between models, cancer types, and clinical decisions. By synthesizing evidence, this study identifies, strengths, limitations, and areas requiring further research.

Methods

The review followed the Joanna Briggs Institute's methodology, Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, and the Population, Concept, and Context mnemonic. Searches were conducted across Embase, IEEE Xplore, PubMed, Scopus, and Web of Science (January 2014-September 2022), targeting English-language quantitative studies in Q1 journ

SUBMITTER: Santos CS 

PROVIDER: S-EPMC11843972 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature