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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Integrating AI Into Emergency Radiology: Promises, Pitfalls, and Practical Approaches
Zachary D Miller1, William E King1, Nathan Cross1
1Department of Radiology, University of Washington School of Medicine, Seattle, WA.
Seminars in Roentgenology
|July 29, 2026
Summary
Artificial intelligence (AI) can aid emergency radiology but requires careful implementation. Real-world impact depends on seamless workflow integration, local validation, and ongoing monitoring to ensure reliable performance and mitigate bias.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Emergency radiology faces high demand, complexity, and workforce shortages.
- Artificial intelligence (AI) offers potential solutions for imaging prioritization, interpretation, and efficiency.
- Evaluating AI's real-world clinical impact requires considering implementation and reliability beyond algorithm performance.
Purpose of the Study:
- To review the role of AI across the emergency radiology workflow.
- To examine current AI capabilities, evidence limitations, and practical implementation considerations.
- To discuss discrepancies between reported and real-world AI performance and potential biases.
Main Methods:
- Narrative review of AI applications in emergency radiology.
- Analysis of evidence spanning pre-acquisition to follow-up stages.
- Integration of published evidence with practical insights on implementation.
Main Results:
- AI applications exist across the entire emergency radiology workflow.
- Gaps persist between reported and real-world AI performance, influenced by human-AI interaction and potential biases.
- Seamless integration into clinical workflows is crucial for AI's true impact.
Conclusions:
- Effective AI implementation in emergency radiology necessitates local validation and continuous performance monitoring.
- Multidisciplinary oversight is essential for identifying variability, mitigating bias, and ensuring reliable AI use.
- Evolving regulatory frameworks and challenges from large language models require ongoing attention.
