Healthcare systems in low socioeconomic regions struggle with numerous challenges, including inadequate infrastructure, severe shortages of healthcare workers, limited access due to geography, and poor health outcomes. Bihar, a state in eastern India and home to more than 120 million people with nearly one-third living in poverty, exemplifies the urgent need for innovative and scalable healthcare solutions. This review evaluates the potential of artificial intelligence (AI) to address healthcare gaps in resource-constrained settings, using Bihar as a case study, and aims to develop practical AI implementation frameworks with global applicability. The approach combines analysis of Bihar’s healthcare data with an assessment of AI applications in other low-resource settings, focusing on interventions that are both cost-effective and scalable. Findings indicate that AI can address critical gaps in diagnostics, disease surveillance, resource management, and care coordination. Notable applications include AI-based diagnostic imaging, outbreak prediction tools, teleconsultation systems with integrated decision support, and technologies for smarter resource allocation. Effective
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