AI-Driven Clinical Decision Support Systems for Resource-Constrained Healthcare Addressing Algorithmic Bias and Deployment Challenges in Low-Income Settings

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.

 

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