Specialist physician scarcity in low- and middle-income countries creates critical healthcare access barriers. This 24-month multi-center study evaluated offline-capable AI-driven Clinical Decision Support Systems across seven sites in Nigeria, India, Kenya, and Brazil. We implemented bias mitigation through transfer learning with local datasets (n=47, 832), federated learning protocols, and uncertainty quantification mechanisms. The system achieved 94. 3% availability despite 62. 1% internet connectivity. Results demonstrated 23. 7% diagnostic accuracy improvement (95% CI: 19. 4–28. 1%, p<0. 001), 31. 2% reduction in unnecessary referrals, and decreased 90-day mortality. Algorithmic bias decreased from 18. 4% to 4. 7% performance gap after local adaptation. Cost-effectiveness analysis showed 28. 77 net savings per encounter. These findings establish that properly adapted AI-CDSS can improve clinical outcomes in resource-constrained settings where specialist expertise is scarcest, with implications for scalable, equitable global health interventions. Full Text Available: AI-Driven Clinical Decision Support Systems for Resource-Constrained Healthcare Addressing Algorithmic Bias and Deployment Challenges in Low-Income Settings
Rahman et al. (Wed,) studied this question.