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Artificial intelligence for detecting traumatic intracranial haemorrhage with CT: A workflow-oriented implementation.
Selim Abed1, Klaus Hergan1, Johannes Pfaff2
1Department of Radiology, University Hospital Salzburg, Paracelsus Medical University, Austria.
The Neuroradiology Journal
|June 3, 2025
Summary
This study evaluated an artificial intelligence (AI) algorithm for detecting intracranial haemorrhages (ICHs) on CT scans. Radiologist acceptance increased with AI performance, but many still preferred physician review.
Area of Science:
- Radiology
- Artificial Intelligence
- Medical Imaging
Background:
- Intracranial haemorrhages (ICHs) are a critical diagnosis on non-contrast CT scans (NCCT).
- Artificial intelligence (AI) offers potential for improving diagnostic accuracy and efficiency in radiology.
Purpose of the Study:
- To assess the performance of an AI algorithm in detecting ICHs on NCCT.
- To evaluate the acceptance of this AI tool among radiologists.
Main Methods:
- AI algorithm performance evaluation for ICH detection on NCCT.
- Radiologist acceptance surveys conducted at 3 and 9 months post-implementation.
Main Results:
- Radiologists' acceptance of the AI tool increased with its performance over time.
- A significant portion of radiologists preferred additional physician review despite comparable costs.
Conclusions:
- Careful software implementation within a robust IT architecture is crucial for AI adoption in clinical practice.
- Balancing AI performance with clinician preference and cost-effectiveness is key for successful integration.
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