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Artificial intelligence in healthcare: Transforming services in low-resource settings—Evidence from Bihar, India

Yogesh Kumar1 ,  Nirangjhana Sivasubramanian1*
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1 Department of Physiology, All India Institute of Medical Sciences Patna, Patna, Bihar, India
AIH, 025420089 https://doi.org/10.36922/AIH025420089
Received: 13 October 2025 | Revised: 23 November 2025 | Accepted: 4 December 2025 | Published online: 24 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

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 deployment depends not only on local adaptation and digital skill-building but also on robust ethical oversight and sustained funding. The evidence indicates that well-targeted AI initiatives can democratize healthcare delivery for underserved populations by enhancing diagnostic accuracy, optimizing scarce resources, extending specialist expertise to remote areas, and reducing health disparities. Achieving these outcomes depends on unified efforts across policy-making, technology development, clinical practice, and community engagement, anchored in principles of equity and inclusivity.

Graphical abstract
Keywords
Artificial intelligence
Healthcare disparities
Resource-constrained settings
Health systems strengthening
Health equity
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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