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Reza Forghani¹

¹Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, Division of Medical Physics, University of Florida College of Medicine, Gainesville, FL; Department of Neurology, Division of Movement Disorders, University of Florida College of Medicine, Gainesville, FL.

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Summary

This summary is machine-generated.

Radiologists need practical guidance for assessing artificial intelligence (AI) tools in clinical radiology. Evaluating AI ensures proper resource allocation and improves patient care, safety, and efficiency.

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Area of Science:

Radiology
Medical Informatics
Artificial Intelligence in Healthcare

Background:

The increasing availability of artificial intelligence (AI) tools in clinical radiology necessitates a structured approach to their evaluation.
Radiologists face choices from various sources, including peers, clinicians, and vendors, for AI acquisition and deployment.

Approach:

This review outlines a systematic framework for assessing AI software in radiology departments.
It details practical considerations crucial for the acquisition and implementation of AI tools.

Key Points:

Familiarity with AI assessment basics is vital for radiologists.
Proper evaluation supports optimal resource allocation.
AI deployment aims to enhance patient care quality, safety, and operational efficiency.

Conclusions:

A practical approach to AI assessment is essential for radiologists.
Informed AI adoption can lead to significant improvements in radiological practice.