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Deployment of Artificial Intelligence in Radiology: Strategies for Success.

Sirui Jiang¹, Syed M A Bukhari¹, Arjun Krishnan²

¹Department of Radiology, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, OH 44106.

AJR. American Journal of Roentgenology

|October 30, 2024

Summary

Artificial intelligence (AI) offers significant potential in radiology, but successful deployment requires addressing regulatory hurdles, ensuring transparency, and implementing rigorous validation and continuous monitoring for AI tools.

Keywords:

artificial intelligence clinical challenges integration radiology validation

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Area of Science:

Medical Imaging and Diagnostics
Health Informatics
Artificial Intelligence in Medicine

Background:

Radiology is a data-intensive field, making it ideal for artificial intelligence (AI) development.
Numerous AI medical devices have received U.S. Food and Drug Administration (FDA) clearance for radiological applications.

Purpose of the Study:

To discuss the practical challenges and strategies for deploying AI tools in radiology.
To highlight key considerations for successful AI implementation in clinical practice.

Main Methods:

Discussion of regulatory processes for AI in radiology.
Exploration of practical challenges including transparency, validation, and workflow integration.
Consideration of standalone and platform-based AI implementation approaches.

Main Results:

Successful AI deployment necessitates rigorous validation, real-world testing, and end-user education.
Continuous feedback and monitoring are crucial for sustained AI performance and adaptation.
Addressing regulatory pathways and practical challenges is essential for realizing AI's benefits.

Conclusions:

Strategic implementation, encompassing regulatory compliance, robust validation, and user training, is key to unlocking AI's potential in radiology.
A proactive approach to challenges ensures the effective and sustained integration of AI tools into radiological practice.