Artificial Intelligence in Medical Imaging: A Review of Radiodiagnosis in Indian Perspectives.
- 1Maulana Azad Medical CollegeNew DelhiIndia.
- 2Department of Data Science and EngineeringIndian Institute of Science Education and ResearchBhopalIndia.
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.
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BibTeX
@article{das2026artificial,
title = {{Artificial Intelligence in Medical Imaging: A Review of Radiodiagnosis in Indian Perspectives.}},
author = {Emily Das and Rasel Mondal and Ashim Dhor and Tanmay Basu},
journal = {The Indian journal of radiology \& imaging},
year = {2026},
month = sep,
doi = {10.1055/s-0046-1827803},
url = {https://europepmc.org/article/MED/42921322},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Medical Imaging: A Review of Radiodiagnosis in Indian Perspectives.
AU - Emily Das
AU - Rasel Mondal
AU - Ashim Dhor
AU - Tanmay Basu
PY - 2026
DA - 2026/09/11
JO - The Indian journal of radiology & imaging
DO - 10.1055/s-0046-1827803
UR - https://europepmc.org/article/MED/42921322
AB - 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.
SN - 0971-3026
SN - 1998-3808
ER -