Structural Barriers and the Future of AI in Global Health: Lessons From Eradicable Diseases

Despite unprecedented scientific and technological capacity, a substantial burden of eradicable and highly reducible diseases of poverty persists in low- and middle-income countries. These conditions continue to cause preventable mortality and morbidity. While artificial intelligence (AI) is widely promoted as a transformative solution to global health challenges, historical evidence demonstrates that technological innovation alone cannot overcome entrenched political, governance, and equity barriers. This narrative review synthesizes evidence from peer-reviewed literature, global health agency reports, and policy analyses to identify conditions that could be eliminated or significantly reduced using existing interventions. Diseases were classified by eradication feasibility and burden, with data compiled on disability-adjusted life years, eradication or control costs, and documented barriers to implementation. Quantitative findings were integrated with a thematic analysis of structural obstacles. Two primary categories emerged. The first includes eradicable diseases such as poliomyelitis, measles, rubella, malaria, tuberculosis, lymphatic filariasis, obstetric fistula, and severe acute malnutrition. The second comprises highly reducible or preventable conditions, including cervical cancer, hepatitis B and C, maternal mortality, neonatal deaths, diarrheal and respiratory infections, mother-to-child HIV transmission, rheumatic heart disease, and cataract blindness. Proven, cost-effective interventions exist for all of these conditions. However, their implementation is persistently hindered by weak health systems, inequitable funding, fragmented governance, and sociocultural barriers. The estimated costs of eradication are modest compared with global expenditures in other sectors. This underscores that political will, rather than technological innovation, is the decisive determinant of progress. The persistent underutilization of current tools is the strongest predictor of how future innovations, including AI, will be deployed. Without structural reforms to strengthen health systems, ensure equitable resource distribution, and embed accountability, innovations, including AI, will not succeed where existing solutions have failed. This review calls for reframing disease eradication as a political and moral imperative, prioritizing governance and system capacity alongside technological innovation to achieve equitable health outcomes.