Investigating the cost-effectiveness of AI in public health
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.
The cost-effectiveness of using artificial intelligence (AI) to assist with chest X-ray interpretation for tuberculosis screening in rural health units in the Philippines has been examined in a new study published in BMC Health Services Research.
According to the researchers, AI-assisted interpretation would be less expensive than manual interpretation, costing approximately Php 877 per person screened compared to Php 1,142. This difference was observed across the entire cohort of 1,000 patients with suspected tuberculosis, over a five-year period.
The study's authors, Harold Henrison Chiu, Bryan Christopher Lao, and Gloanne C. Adolor, suggest that AI could potentially extend expert-level TB screening support to geographically isolated or disadvantaged communities where expertise is scarce, making screening more affordable, sustainable, and equitable. However, the researchers acknowledge that the actual effectiveness and cost-savings of AI may depend on local conditions, such as the specific costs and diagnostic accuracy in a given context.
Rather than advocating for immediate nationwide implementation, the researchers recommend starting with targeted pilot programs in underserved rural health units, paired with local validation, quality assurance, monitoring and budget assessment before any broader rollout.
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