Jul 2026· Indian Journal of Radiology and Imaging· 0 citations· 60 references
TL;DR
The challenges in going from AI model performance to better outcomes are discussed, including the impact of false positives on workflow, ethical and regulatory issues, and implementation challenges.
Abstract
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.
This narrative review examines the role of AI across the emergency radiology workflow through three lenses: current capabilities, limitations of the supporting evidence, and practical considerations for clinical implementation, integrating published evidence with practical insights.
Zachary D. Miller, William E. King, N. Cross et al.· Seminars in roentgenology· 0 citations
This review examines how AI is reshaping radiology workflow, summarises clinically relevant applications across imaging modalities, evaluates evidence regarding diagnostic performance and operational efficiency, and discusses implementation, ethics, regulation, workforce, economic, and equity-related considerations.
Neelam Rao Bharti, Deeksha Jaiswal, Nidhi Goswami et al.· Genetics and Molecular Resea...· 0 citations
The findings highlight the need for evidence from large, multicenter, prospective trials and evaluation frameworks that reflect the consequences of clinical decision-making, as well as further exploration of safeguards to monitor and address mismatches between training data and incoming scans during deployment.
Lisa Koopmans, Fernando Vega Lara, Christian Roest et al.· Abdominal Radiology· 0 citations
Abstract Artificial intelligence (AI) in pediatric imaging is evolving rapidly but lags behind and remains fundamentally distinct from adult radiology AI, due to various factors, such as developmental variability, differing disease epidemiology, unique imaging constraints, and limited data ecosystems. This narrative re...
Amit Gupta, Anjali Agrawal, Manisha Jana· Indian Journal of Radiology...· 0 citations
Artificial intelligence (AI) has become one of the most actively discussed tools in modern day radiology, promising to help interpret X-rays, CT scans, and MRIs alongside human radiologists. It has matched or exceeded human accuracy outright, particularly in a few narrow tasks. Furthermore, AI has come at a time when i...
The panel gives key recommendations, emphasizing the importance of an implementation roadmap to establish a dedicated pediatric AI infrastructure and standards that are essential to ensure diagnostic accuracy, workflow efficiency, and optimal clinical outcomes for children while minimizing bias and protecting patient s...
Mario Sinti-Ycochea, M. Sammer, Susan Sotardi et al.· AJR. American journal of roe...· 0 citations
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