ADVANCING MRI TECHNOLOGY IN INDIA: A COMPARATIVE STUDY OF TRADITIONAL AND AI-BASED MRI MACHINES WITH REGULATORY CHALLENGES AND SOLUTIONS

Abstract

Purpose: This study evaluates the clinical and operational performance of Artificial Intelligence (AI)-based Magnetic Resonance Imaging (MRI) systems compared with traditional MRI, while examining regulatory and infrastructural challenges to AI adoption in India.

Materials and Methods: This study employed mixed-methods, non-interventional design based on secondary data analysis. Quantitative performance metrics were synthesized from peer-reviewed publications, regulatory submissions, international case studies, and vendor-reported technical documentation comparing traditional MRI and AI-enabled MRI systems. Qualitative analysis focused on regulatory frameworks, policy documents from the Central Drugs Standard Control Organization (CDSCO), and published expert commentaries to identify adoption challenges within the Indian context. Performance indicators included diagnostic accuracy, scan duration, image quality (SNR, CNR, artifacts), operational efficiency, and cost-effectiveness. Statistical analysis used t-tests and chi-square tests, with significance at p < 0.05.

Results: AI-based MRI systems demonstrated 23% higher diagnostic accuracy, 35% shorter scan times, and 28% better image quality relative to traditional systems. Operational efficiency improved with a 52% increase in patient throughput, 67% reduction in repeat scans, and halved technologist time per scan. Cost-effectiveness analysis indicated a break-even point within 3.5 years. Key barriers to adoption included lack of standardized evaluation protocols, limited IT infrastructure in tier-2/3 cities, and a severe skilled workforce shortage (severity score 9.1/10).

Conclusion: AI-based MRI systems significantly enhance diagnostic precision, efficiency, and economic sustainability compared to conventional MRI. However, large-scale adoption in India requires regulatory reform, infrastructure strengthening, and workforce training. Addressing these systemic barriers through harmonized standards, centers of excellence, and quality assurance frameworks can accelerate AI-MRI integration and improve national healthcare outcomes.