A Systems Approach toward AI-Enabled Radiology Dictation for Workflow Efficiency in India.
- 1Department of RadiologyPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPennsylvaniaUnited States.
Abstract
Introduction
Radiology reporting remains a major workflow bottleneck, with inefficiencies in dictation, editing, and report finalization leading to delays and an increased documentation burden. AI-enabled dictation systems combine automatic speech recognition (ASR), natural language processing (NLP), structured reporting, and real-time quality assurance to streamline workflows while preserving radiologist oversight.Technological considerations
Current evidence suggests that human-in-the-loop systems improve reporting efficiency, consistency, and documentation quality more effectively than standalone speech recognition or fully automated reporting approaches.India-specific considerations
In India, implementation presents unique challenges related to multilingual clinical environments, infrastructure variability, and evolving digital health frameworks such as the Ayushman Bharat Digital Mission.Implementation and governance
Successful adoption, therefore, depends not only on model performance but also on workflow integration, interoperability, governance, and local validation. AI-enabled dictation represents a shift from simple speech-to-text transcription to structured, interoperable, and clinically integrated reporting systems that have the potential to improve workflow efficiency, report quality, and clinical communication.
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BibTeX
@article{tripathi2026systems,
title = {{A Systems Approach toward AI-Enabled Radiology Dictation for Workflow Efficiency in India.}},
author = {Satvik Tripathi and Tessa S Cook},
journal = {The Indian journal of radiology \& imaging},
year = {2026},
month = sep,
doi = {10.1055/s-0046-1827765},
url = {https://europepmc.org/article/MED/42921362},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A Systems Approach toward AI-Enabled Radiology Dictation for Workflow Efficiency in India.
AU - Satvik Tripathi
AU - Tessa S Cook
PY - 2026
DA - 2026/09/03
JO - The Indian journal of radiology & imaging
DO - 10.1055/s-0046-1827765
UR - https://europepmc.org/article/MED/42921362
AB - Introduction Radiology reporting remains a major workflow bottleneck, with inefficiencies in dictation, editing, and report finalization leading to delays and an increased documentation burden. AI-enabled dictation systems combine automatic speech recognition (ASR), natural language processing (NLP), structured reporting, and real-time quality assurance to streamline workflows while preserving radiologist oversight.Technological considerations Current evidence suggests that human-in-the-loop systems improve reporting efficiency, consistency, and documentation quality more effectively than standalone speech recognition or fully automated reporting approaches.India-specific considerations In India, implementation presents unique challenges related to multilingual clinical environments, infrastructure variability, and evolving digital health frameworks such as the Ayushman Bharat Digital Mission.Implementation and governance Successful adoption, therefore, depends not only on model performance but also on workflow integration, interoperability, governance, and local validation. AI-enabled dictation represents a shift from simple speech-to-text transcription to structured, interoperable, and clinically integrated reporting systems that have the potential to improve workflow efficiency, report quality, and clinical communication.
SN - 0971-3026
SN - 1998-3808
ER -