AI-Driven Digital Health Ecosystems: Empowering India's Economy Through Innovation and Equity in Healthcare Access

Abstract

India’s healthcare sector is revolutionised with the adoption of AI driven digital health ecosystems to enhance accessibility, efficiency and affordability. This thesis looks into the integration of AI based models in healthcare dealing with predictive analytics, automated diagnostics and personalized treatment. Four healthcare datasets were processed through four AI algorithms to evaluate them: Random Forest, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Long Short Term Memory (LSTM). Accurate results of CNN, Random Forest, SVM and LSTM for disease classification are 94.2, 91.8, 89.5 and 87.6, respectively. AI based solutions proved to be 63% faster than traditional healthcare models in terms of diagnostic speed, and reduced misdiagnosis rate by 28%. Moreover, AI based predictive models improved resource allocation efficiency by 45 % and saved hospital operational cost by 22%. Although AI brings in advantages but it entails the problems like data privacy, algorithmic bias and AI literacy gap to be taken care of for smooth deployment. The focus of this study is on building a Human Centric AI framework where an AI is created to support the medical profession, instead of replacing them. This highlights the transformative potential of AI for healthcare in India and establishes a reference point for future research on ethical integration and equity of access to healthcare through AI.