Beyond the Algorithm: What Will It Take for Artificial Intelligence to Succeed in Indian Radiology?
- 1Centre for Responsible Autonomous Systems in Healthcare (CRASH Lab), Koita Centre for Digital Health, Ashoka UniversityDelhiIndia.
- 2Department of RadiodiagnosisMax HealthcareSaketDelhiIndia.
- 3Department of RadiodiagnosisHomi Bhabha Cancer Hospital and Research CentreNew ChandigarhIndia.
Abstract
Artificial intelligence (AI) is entering Indian radiology faster than the capacity of existing workflows to adapt, with increasing claims of expert-level algorithmic performance, a regulatory architecture beginning to form, and rising clinician interest. Yet translation of technically capable tools into reliable clinical value remains uneven. This perspective-style review synthesizes recent literature on radiology AI deployment, workflow integration, validation, and governance, with observations across public teaching hospitals, private diagnostic networks, and teleradiology settings. We argue that while the dominant discourse around medical AI remains algorithm-centric, the dominant problem in Indian radiology is workflow-centric. This mismatch is a major reason why promising tools often fail to deliver real-world value. We map the contemporary Indian radiology workflow, identify recurring structural bottlenecks, and locate the points at which AI can meaningfully augment clinical practice. We examine priority use cases according to clinical maturity, describe scenarios in which deployment may be unsafe or premature, and propose a staged departmental readiness framework aligned with India's emerging digital-health and AI-governance landscape. For radiologists, the article offers a practical way to evaluate AI tools against departmental realities. For administrators and policy stakeholders, it outlines a framework for institutional investment, validation, and governance. For developers and vendors, it clarifies where Indian clinical workflows may accept augmentation and where integration is likely to be difficult. Our central position is that Indian radiology departments should build workflow integration, local validation capacity, post-deployment audit mechanisms, and governance structures before acquiring AI tools, so that adoption is safe, measurable, and clinically meaningful.
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BibTeX
@article{datta2026beyond,
title = {{Beyond the Algorithm: What Will It Take for Artificial Intelligence to Succeed in Indian Radiology?}},
author = {Suvrankar Datta and Prerna Priyadarshini and Antarjot Kaur Rekhi},
journal = {The Indian journal of radiology \& imaging},
year = {2026},
month = sep,
doi = {10.1055/s-0046-1829099},
url = {https://europepmc.org/article/MED/42921396},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Beyond the Algorithm: What Will It Take for Artificial Intelligence to Succeed in Indian Radiology?
AU - Suvrankar Datta
AU - Prerna Priyadarshini
AU - Antarjot Kaur Rekhi
PY - 2026
DA - 2026/09/29
JO - The Indian journal of radiology & imaging
DO - 10.1055/s-0046-1829099
UR - https://europepmc.org/article/MED/42921396
AB - Artificial intelligence (AI) is entering Indian radiology faster than the capacity of existing workflows to adapt, with increasing claims of expert-level algorithmic performance, a regulatory architecture beginning to form, and rising clinician interest. Yet translation of technically capable tools into reliable clinical value remains uneven. This perspective-style review synthesizes recent literature on radiology AI deployment, workflow integration, validation, and governance, with observations across public teaching hospitals, private diagnostic networks, and teleradiology settings. We argue that while the dominant discourse around medical AI remains algorithm-centric, the dominant problem in Indian radiology is workflow-centric. This mismatch is a major reason why promising tools often fail to deliver real-world value. We map the contemporary Indian radiology workflow, identify recurring structural bottlenecks, and locate the points at which AI can meaningfully augment clinical practice. We examine priority use cases according to clinical maturity, describe scenarios in which deployment may be unsafe or premature, and propose a staged departmental readiness framework aligned with India's emerging digital-health and AI-governance landscape. For radiologists, the article offers a practical way to evaluate AI tools against departmental realities. For administrators and policy stakeholders, it outlines a framework for institutional investment, validation, and governance. For developers and vendors, it clarifies where Indian clinical workflows may accept augmentation and where integration is likely to be difficult. Our central position is that Indian radiology departments should build workflow integration, local validation capacity, post-deployment audit mechanisms, and governance structures before acquiring AI tools, so that adoption is safe, measurable, and clinically meaningful.
SN - 0971-3026
SN - 1998-3808
ER -