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AI in Action: A Road Map From the Radiology AI Council for Effective Model Evaluation and Deployment.

Hari Trivedi¹, Bardia Khosravi², Judy Gichoya³

¹Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia; Chair, Radiology AI Council; Codirector, HITI Lab; Director, AI Image Extraction Core, Emory University.

Journal of the American College of Radiology : JACR

|May 25, 2025

Summary

A Radiology AI Council developed a rubric to standardize the evaluation and deployment of artificial intelligence (AI) models in clinical workflows. This framework ensures transparent and holistic AI model assessment for improved efficacy and safety in radiology.

Keywords:

AI model deployment model evaluation

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Radiology
Medical Informatics
Artificial Intelligence

Background:

The increasing integration of artificial intelligence (AI) into radiology necessitates standardized evaluation and deployment processes.
Challenges in AI deployment include real-world performance, workflow integration, and resource allocation.

Purpose of the Study:

To outline the creation of a Radiology AI Council and a framework (rubric) for evaluating and onboarding AI models.
To ensure a standardized and transparent process for selecting AI models in radiology.

Main Methods:

Establishment of a Radiology AI Council at an academic center.
Development of a comprehensive rubric to assess AI models.
Evaluation of 13 AI models over an 8-month period using the rubric.

Main Results:

The rubric addresses key deployment challenges: real-world performance, workflow, resources, ROI, and health system impact.
Initial evaluation of 13 models provided insights into the rubric's application.
The process emphasized holistic evaluation beyond performance metrics.

Conclusions:

A standardized, transparent rubric is crucial for successful AI integration in radiology.
Holistic evaluation and objective assessment are vital for improving AI efficacy and safety.
The developed framework supports the effective onboarding of AI models into clinical workflows.