Investigating the cost-effectiveness of AI in public health
· Medical Xpressby Danika Geronimo, Ateneo de Manila University
edited by Swati Mestri, reviewed by Robert Egan
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In the Philippines, public health care remains inaccessible to many because of factors including high costs and the limited availability of medical experts. A new study by Ateneo researchers explores how artificial intelligence (AI) could help address this gap by examining the cost-effectiveness of AI-assisted chest radiograph (X-ray) interpretation.
This is particularly important for people with tuberculosis (TB), because finding the disease early can mean receiving care before it becomes severe and causes irreversible damage. According to the World Health Organization, an estimated 739,000 people in the Philippines developed tuberculosis in 2024, accounting for 6.8% of the 10.8 million TB cases worldwide.
As with most other diseases, early detection is essential. In geographically isolated or disadvantaged communities and rural health units, even if a patient can get an X-ray, waiting for a radiologist or teleradiology service to interpret it may take a long time. For some, that wait can mean another trip to a health facility, additional expenses, time away from work or a missed opportunity for continued care.
Harold Henrison Chiu, Bryan Christopher Lao and Gloanne C. Adolor published their research, "Cost-effectiveness evaluation of artificial intelligence-assisted chest radiograph interpretation for tuberculosis screening in rural health units in the Philippines," in the August 2026 issue of BMC Health Services Research.
The researchers developed a decision-analytic model based on a theoretical annual cohort of 1,000 patients with suspected TB undergoing chest radiography in rural health units. The analysis considered costs and outcomes over five years, including AI software and operating expenses, radiologist reading fees and confirmatory GeneXpert testing.
The study's model projected that the AI-assisted strategy would cost an estimated Php 877,330 annually, compared with Php 1.14 million for manual interpretation. Divided across the 1,000 people screened, that translated to about Php 877 per person with AI-assisted interpretation, versus about Php 1,142 per person with manual interpretation. The AI-assisted approach was less expensive in the model.
But the researchers suggest AI's significance goes beyond its ability to read an X-ray efficiently. "For resource-constrained communities, the most important question is therefore not whether AI can outperform or assist an expert reader, but whether it can extend expert-level support to places where expertise is scarce in a way that is affordable, sustainable, and equitable," the researchers said.
The researchers note that, if properly integrated into existing TB programs through portable digital X-rays and systems that can operate with limited connectivity, AI could help bring TB screening closer to underserved communities. The goal, they emphasize, should not be to introduce another high-tech tool into health care, but to narrow existing geographic disparities.
However, the findings also highlight the importance of local conditions. When a lower manual or teleradiology reading fee was used, or when diagnostic performance estimates from a Philippine scenario were applied, AI remained more effective but was no longer necessarily cost-saving.
The study is based on a theoretical cohort and assumptions about costs and diagnostic accuracy, while AI-assisted findings would still require confirmatory testing. Rather than immediate nationwide adoption, the researchers recommend starting with targeted pilot implementation in underserved rural health units, alongside local validation, quality assurance, monitoring and budget assessment.
More information
Harold Henrison Chiu et al, Cost-effectiveness evaluation of artificial intelligence-assisted chest radiograph interpretation for tuberculosis screening in rural health units in the Philippines, BMC Health Services Research (2026). DOI: 10.1186/s12913-026-15449-3
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Infectious diseasesCommon illnesses & PreventionDiagnostic radiologyPreventive medicine Provided by Ateneo de Manila University Who's behind this story?
Swati Mestri
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