In the Philippines, public healthcare remains inaccessible to many due to a variety of factors, including the high cost and limited availability of medical experts.
A new study by Ateneo researchers explores how artificial intelligence (AI) can help address this gap by looking at the cost-effectiveness of AI-assisted chest radiograph (X-ray) interpretation.
This is particularly vital for people with tuberculosis (TB), as finding the disease early can mean receiving the care they need before it becomes severe and causes irreversible damage. According to the World Health Organization, in 2024 alone an estimated 739,000 people in the Philippines developed tuberculosis, 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 is lucky enough to have an X-ray taken, waiting for a radiologist or teleradiology services 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.
Dr. Harold Chiu, Dr. Bryan Lao, and Dr. Gloanne Adolor developed a decision-analytic model based on a theoretical annual cohort of 1,000 presumptive TB patients 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.
Based on the study’s model-based projections, the AI-assisted strategy would entail an estimated annual cost of Php 877,330, compared with Php 1.14 million for manual interpretation. Divided across the 1,000 individuals screened, this translated to about Php 877 per person with AI-assisted interpretation, versus about Php 1,142 per person using manual interpretation. The use of AI promised to be economical.
Accessibility beyond efficiency
But the researchers suggest that the significance of AI goes beyond its capability 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 healthcare, but to narrow existing geographic disparities.
Importance of local conditions
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.
For a country that carries a significant share of the world’s tuberculosis burden and struggles to provide universal healthcare to its citizens, the question may ultimately be less about bringing the newest technology into healthcare and more about where that technology can help deliver expertise to meet the realities of people with the least access to it. — Danika Geronimo, Ateneo Research Communications

SOURCE: https://archium.ateneo.edu/gsb-pubs/87/

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 the BMC Health Services Research journal.