Impact of AI-Assisted Chest Radiography as a Triage Tool for Molecular Lab Testing in Rural Philippines: A Systematic Review of Diagnostic Yield and Health Equity

Artificial intelligence (AI)–assisted chest radiography has increasingly been utilized as a diagnostic triage tool for tuberculosis (TB) screening and molecular laboratory testing in resource-limited healthcare settings. In the Philippines, limited access to radiologists, molecular diagnostic facilities, and healthcare infrastructure in rural and geographically isolated communities continues to contribute to delayed TB diagnosis and treatment. AI-assisted chest radiography may improve diagnostic efficiency, optimize molecular testing utilization, and strengthen equitable access to healthcare services. This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines and was registered in PROSPERO under registration number: CRD420261395186. A comprehensive literature search was conducted across PubMed/MEDLINE, Scopus, and Web of Science, supplemented by manual searches of institutional and organizational reports. Studies published between January 2019 and March 2026 were screened according to predefined inclusion and exclusion criteria. Eligible studies evaluated AI-assisted chest radiography as a screening or triage tool for molecular diagnostic testing in rural or resource-limited settings. Data was synthesized narratively. Thirty-three studies met the inclusion criteria. The findings demonstrated that AI-assisted chest radiography consistently achieved high sensitivity for TB screening and effectively functioned as a triage mechanism for molecular diagnostic testing including GeneXpert, Truenat, and PCR assays. Several studies reported improved diagnostic yield, reduced unnecessary molecular testing, enhanced workflow efficiency, and improved treatment linkage. Portable AI-assisted chest X-ray systems also demonstrated operational feasibility in geographically isolated and disadvantaged communities. However, variability in specificity, screening thresholds, infrastructure readiness, and implementation capacity across healthcare settings remained important challenges. AI-assisted chest radiography represents a promising strategy for strengthening TB diagnostic pathways, optimizing molecular laboratory utilization, and improving equitable access to diagnostic services in resource-constrained healthcare settings. Further prospective implementation studies, cost-effectiveness analyses, and health systems evaluations are needed to support large-scale integration of AI-assisted diagnostic technologies within Philippine healthcare systems.

artificial intelligence, chest radiography, tuberculosis, molecular diagnostic testing

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