Machine Learning in Health: A Systematic Review

• Alessandra R. C. Padovam UNICAMP
• Danilo Rodrigues Pereira UNICAMP
• Romis Attux UNICAMP

DOI: https://doi.org/10.5753/sbcas_estendido.2025.6992

Resumo

The aim of this study is to construct a taxonomy of Machine Learning (ML) techniques most commonly employed in health applications. The systematic review was performed between October 2023 and November 2024, and comprised articles published from 2013 to 2023. The search for relevant references was done on Pubmed and MEDLINE databases, and the screening articles were analysed by two reviewers independently through the Covidence platform. We identified 2,714 articles resulting in 65 health problems related to some ML technique and 8 types of learning. This research brings trends in use of ML for application in the field of medicine in general.

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