Artificial Intelligence in Emergency Radiology: Current Applications, Challenges, and Future Directions.
- 1Teleradiology Solutions/dAIgnostiXDelhiIndia.
- 2TMC, a Unilabs Company, BarcelonaSpain.
- 3Department of Diagnostic RadiologyYale University, New Haven, ConnecticutUnited States.
Abstract
This is a narrative review of recent peer-reviewed literature on artificial intelligence (AI) in emergency imaging spanning patient scheduling, image optimization, detection of critical findings, communication, and regulatory oversight. We review the current AI applications in emergency radiology and summarize clinically meaningful performance metrics. There are multiple instances of high performance in identifying imaging findings and prioritizing worklists, but limited prospective data demonstrating patient outcome improvements. We discuss the challenges in going from AI model performance to better outcomes, including the impact of false positives on workflow, ethical and regulatory issues, and implementation challenges. Practical tips for the evaluation, adoption, and monitoring of AI tools are outlined. Regulatory frameworks are in evolution and lag this rapidly changing landscape. Radiologist oversight remains critical at all steps to ensure safe and effective use of AI tools.
Related
- Artificial Intelligence in Civil Engineering a Comprehensive Review of Applications, Challenges, and Future Directions
- Artificial Intelligence and AI-Adjacent Digital Technologies in Nursing Practice Within Gastroenterology: A Scoping Review of Current Applications, Challenges, and Future Directions.
- Current Advances in Spatial Pathology of Nasal Polyps: Challenges, Opportunities for Artificial Intelligence, and Future Directions
- Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions
- Artificial intelligence in pediatric head and neck tumor imaging: a mini review of current applications and future directions
BibTeX
@article{agrawal2026artificial,
title = {{Artificial Intelligence in Emergency Radiology: Current Applications, Challenges, and Future Directions.}},
author = {Anjali Agrawal and Fausto Labruto and Melissa A Davis},
journal = {The Indian journal of radiology \& imaging},
year = {2026},
month = jul,
doi = {10.1055/s-0046-1825538},
url = {https://europepmc.org/article/MED/42921380},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Emergency Radiology: Current Applications, Challenges, and Future Directions.
AU - Anjali Agrawal
AU - Fausto Labruto
AU - Melissa A Davis
PY - 2026
DA - 2026/07/15
JO - The Indian journal of radiology & imaging
DO - 10.1055/s-0046-1825538
UR - https://europepmc.org/article/MED/42921380
AB - This is a narrative review of recent peer-reviewed literature on artificial intelligence (AI) in emergency imaging spanning patient scheduling, image optimization, detection of critical findings, communication, and regulatory oversight. We review the current AI applications in emergency radiology and summarize clinically meaningful performance metrics. There are multiple instances of high performance in identifying imaging findings and prioritizing worklists, but limited prospective data demonstrating patient outcome improvements. We discuss the challenges in going from AI model performance to better outcomes, including the impact of false positives on workflow, ethical and regulatory issues, and implementation challenges. Practical tips for the evaluation, adoption, and monitoring of AI tools are outlined. Regulatory frameworks are in evolution and lag this rapidly changing landscape. Radiologist oversight remains critical at all steps to ensure safe and effective use of AI tools.
SN - 0971-3026
SN - 1998-3808
ER -