A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation.

This systematic review evaluates the application of machine learning (ML) models for diagnosing pulmonary tuberculosis and their potential to inform nursing practice and implementation strategies. Studies published between 2019 and 2024 were systematically identified through searches in Scopus, PubMed, Medline, ScienceDirect, CINAHL Plus with Full Text, Clinical Key, Ovid, EMBASE, and Web of Science. The review adhered to PRISMA guidelines, with rigorous inclusion and exclusion criteria applied.