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Purpose: To aim of the study was to analyze the development of AI-driven healthcare systems in rural India. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: AI-driven healthcare systems are increasingly being integrated into rural India's healthcare infrastructure to overcome traditional barriers such as limited access to healthcare facilities and skilled medical professionals. These systems utilize AI technologies like machine learning and natural language processing to enhance diagnostic accuracy, optimize treatment planning, and improve patient outcomes remotely. Challenges remain, including infrastructure limitations and the need for customized AI solutions that address regional healthcare needs effectively. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & socio-technical systems theory may be used to anchor future studies on development of AI-driven healthcare systems in rural India. Implement comprehensive training programs for healthcare providers to build confidence and competence in using AI-driven tools. Policymakers should prioritize investments in digital infrastructure, including reliable internet connectivity and digital health platforms, to support the implementation of AI-driven healthcare systems in rural areas.
Alia Bhatt (2024) studied this question.