Artificial Intelligence-Driven Healthcare for India: A Comprehensive Review, Cross-National Evaluation, and Architectural Proposal of the Sanjeevani × AI Integrated Health Intelligence Platform
Artificial Intelligence-Driven Healthcare for India: A Comprehensive Review, Cross-National Evaluation, and Architectural Proposal of the Sanjeevani × AI Integrated Health Intelligence Platform
Authors:
Sayan Kumar Nandi
Department of Computer Science and Engineering KCC Institute of Technology Management
Affiliated to Dr. A. P. J. Abdul Kalam Technical University, India
ORCID: https://orcid.org/0009-0009-1938-4597
Abstract—India confronts a structurally singular healthcare crisis char-acterised by a quadruple disease burden— communicable diseases, non-communicable diseases (NCDs), mental health disorders, and nutritional deficiencies—superimposed on a physician-to-patient ratio of 1:1,511, starkly below the WHO-recommended 1:1,000. Despite the nation’s demonstrated capabilities in information technology and the government’s Ayushman Bharat Digital Mission (ABDM), no indige-nous, privacy-compliant, multilingual artificial intelligence (AI) platform has been deployed at scale to bridge this structural gap. This paper presents Sanjeevani × AI, a federated, multimodal health intelligence platform architecturally designed for India’s heterogeneous population and constrained-resource clinical environments. The system integrates five synergistic modules: (i) an NLP-driven, multilingual symptom triage engine supporting 22 scheduled languages; (ii) a computer-vision (CV) pipeline for pharmaceutical identification and dermatological screening;
(iii) a sensor-fusion module for wearable-derived longitudinal biomarker analysis; (iv) a federated predictive analytics layer for outbreak detection and chronic disease progression modelling; and (v) a compliance and explainability layer co-designed with the Digital Personal Data Protection (DPDP) Act 2023. Through a structured cross-national evaluation of analogous AI deployments in the United States, United Kingdom, China, Singapore, and sub-Saharan Africa, we identify 27 context-specific limita-tions that preclude direct technology transfer and motivate the indigenous design philosophy of Sanjeevani × AI. The proposed architecture is benchmarked against established frameworks, and a phased validation strategy employing federated learning on the National Health Stack infrastructure is delineated. We argue that Sanjeevani × AI offers a replicable blueprint for AI-augmented primary care in low- and middle-income countries (LMICs) globally.
Index Terms—Artificial intelligence in healthcare, federated learning, multilingual NLP, primary healthcare India, Ayushman Bharat Digital Mission, DPDP Act 2023, computer vision diagnostics, wearable sensor fusion, health informatics LMICs, explainable AI, disease surveillance.