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Navigating the Frontier of artificial intelligence implementation in radiology - part 1: Performance assessment.

Maguy Farhat^1,2, Samir A Dagher¹, Burak Berksu Ozkara^1,3

¹Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, USA.

The Neuroradiology Journal

|December 18, 2025

Summary

Artificial intelligence (AI) in medical imaging faces challenges in clinical use due to performance evaluation issues. This study addresses data heterogeneity, metrics, and access to improve AI deployment in radiology.

Keywords:

Artificial intelligence computer-aided diagnosis diagnosis machine learning performance assessment prognosis radiology

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Despite significant growth in AI research and investment in medical imaging, clinical translation remains limited.
A critical gap exists due to the lack of standardized guidelines for evaluating AI model performance and ethical considerations.

Purpose of the Study:

To provide a practical perspective on the performance challenges hindering AI implementation in radiology.
To explore mitigation strategies for these challenges, focusing on data heterogeneity, performance metrics, and data access.

Main Methods:

Exploration of challenges in AI performance evaluation within radiology.
Focus on data heterogeneity, choice and interpretability of performance metrics, and data access issues.
Discussion of potential opportunities for overcoming these hurdles.

Main Results:

Identified key performance evaluation challenges for AI in radiology, including data variability and metric interpretation.
Highlighted the need for improved data access and standardized performance metrics.
Emphasized the gap between AI's theoretical potential and its practical clinical application.

Conclusions:

Addressing performance evaluation challenges is crucial for the practical integration of AI in clinical radiology.
Established guidelines are imperative for the safe and efficient deployment of AI in medical imaging.
Bridging the gap between AI potential and clinical implementation requires focused efforts on performance and data standardization.