Artificial intelligence in rural healthcare in India: applications, challenges and a strategic implementation framework

Harsh V. Baranwal; Amrita; Rahul Ranjan; Paridhi

doi:10.18203/2394-6040.ijcmph20261452

Authors

Harsh V. Baranwal Department of General Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
Amrita Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
Rahul Ranjan Department of General Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
Paridhi Department of Obstetrics and Gynaecology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India

DOI:

https://doi.org/10.18203/2394-6040.ijcmph20261452

Keywords:

Artificial intelligence, Rural healthcare, Telemedicine, Primary care, Health systems, India

Abstract

India’s healthcare system is marked by a paradox wherein innovative medicine technologies exist alongside significant accessibility issues in rural areas where the majority (more than 66%) of its citizens live. As much as 80% of specialist positions at community and primary health centres in rural India are vacant, contributing to diagnostic inefficiencies and placing an emphasis on chronic disease prevalence. The purpose of this paper is to examine the role of artificial intelligence (AI) in enhancing healthcare services in rural India based on literature reviews from 2015 to 2025. The integration of machine learning algorithms and computer vision has enormous potential to bypass geographical and personnel limitations. Potential use cases involve tele-radiology services powered by AI for detecting tuberculosis and stroke, edge AI technologies that can perform offline diagnostics, decision support systems in telemedicine, and epidemic predictions. However, widespread adoption faces multiple socio-digital and structural challenges, including the lack of independent studies performed in rural settings, insufficient basic infrastructure (e.g., power supply and internet connectivity), and the potential of creating biased algorithms when training is primarily performed using historical data unrelated to local conditions. For optimal utilization of AI, a phased approach focusing on augmenting the intelligence of medical professionals is crucial. The government should concentrate on developing digital public infrastructure, engaging public-private partnerships, and creating context-specific, offline-ready solutions.

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