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Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Vikash Gupta1, Barbaros Erdal1, Carolina Ramirez2
1Mayo Clinic, Jacksonville, FL, United States.
JMIR AI
|December 9, 2024
Summary
Artificial intelligence (AI) can enhance medical imaging efficiency, but clinical translation faces challenges. This paper explores AI in radiology workflows and introduces the Medical Open Network for AI (MONAI) to bridge the gap.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology Workflow
Background:
- Radiologist workload is increasing due to growing medical imaging data.
- AI shows potential to improve medical image analysis efficiency.
- A gap exists between AI research and clinical application in radiology.
Purpose of the Study:
- Provide an overview of AI in medical imaging.
- Highlight the importance of standards in radiology workflows.
- Identify challenges in deploying AI in clinical settings.
Main Methods:
- Examined current radiology workflows and AI implementation challenges.
- Developed a taxonomy of AI use cases with real-world integration examples.
- Introduced the Medical Open Network for AI (MONAI) as a solution.
Main Results:
- Identified significant hurdles in integrating AI into hospital radiology workflows.
- Demonstrated practical AI integration examples within hospitals.
- Proposed MONAI as a tool for reproducible deep learning solutions.
Conclusions:
- Addressing AI implementation challenges requires standardized approaches.
- MONAI offers a framework for successful AI integration in radiology.
- Bridging the research-to-clinical translation gap is crucial for AI adoption.
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