Artificial Intelligence in Medical Imaging: A Review of Radiodiagnosis in Indian Perspectives

Abstract

Abstract Artificial intelligence (AI) has become the most transformative development in radiology since digital imaging, with deep learning algorithms demonstrating validated—and in controlled settings superior—performance across pulmonary nodule detection, breast cancer screening, tuberculosis (TB) triage, acute stroke identification, and glioma segmentation. Over 950 AI/machine learning–enabled medical devices have received clearance from the U.S. Food and Drug Administration as of 2024, ∼75% within radiology, yet a consequential knowledge gap persists: fewer than 30% of practising radiologists can critically evaluate an AI study and fewer than 20% have received formal AI training. This review addresses that gap by providing a clinically accessible account of core AI concepts, major neural network architectures—including convolutional neural networks, U-Net, generative adversarial networks, vision transformers, and foundation models—the complete model development and validation workflow, and validated clinical applications across all major imaging modalities. We give dedicated attention to the Indian context, where an acute radiologist deficit of 1:50,000 against the World Health Organization-recommended ratio of 1:10,000, a 26% share of the global TB burden, and heterogeneous imaging infrastructure make AI both urgently necessary and uniquely demanding of local validation; we discuss the Ayushman Bharat Digital Mission, federated learning, and the domestic ecosystem of Qure.ai, Niramai, and Sigtuple as structural enablers, as well as algorithmic bias, explainability, and regulatory frameworks. We conclude that AI literacy is no longer optional: the radiologist's role is not threatened by AI but transformed by it—from film reader to AI-augmented clinical imaging physician capable of extending world-class diagnostic care to underserved populations across India and globally.