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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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How does artificial intelligence in radiology improve efficiency and health outcomes?
Kicky G van Leeuwen1, Maarten de Rooij2, Steven Schalekamp2
1Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands. kicky.vanleeuwen@radboudumc.nl.
Pediatric Radiology
|June 12, 2021
Summary
Artificial intelligence (AI) in radiology aims to enhance healthcare and cut costs. Current evidence is limited, with more real-world data needed to determine AI's true clinical value and guide future decisions.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Artificial intelligence (AI) was introduced to radiology with the promise of improving healthcare and reducing costs.
- The actual impact of AI on clinical practice remains largely unknown.
- The market for AI in radiology is still maturing.
Purpose of the Study:
- To evaluate whether AI has fulfilled its promise in radiology.
- To describe six clinical objectives AI can support.
- To provide use cases and scientific evidence for AI's impact.
Main Methods:
- Described six clinical objectives for AI support in radiology.
- Provided use case examples.
- Assessed scientific evidence using a hierarchical model of efficacy.
Main Results:
- AI can support objectives like workflow efficiency, reduced reading time, and dose reduction.
- AI may aid in earlier disease detection, improved diagnostic accuracy, and personalized diagnostics.
- Limited data exists on AI's contribution to clinical practice.
Conclusions:
- The AI market in radiology is still developing.
- More real-world monitoring is necessary to ascertain AI's value.
- Informed decisions on AI development, procurement, and reimbursement require further evidence.
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