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

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Review of Artificial Intelligence Training Tools and Courses for Radiologists.

Michael L Richardson¹, Scott J Adams², Atul Agarwal³

¹Department of Radiology, University of Washington, Seattle, Washington.

Academic Radiology

|March 14, 2021

Summary

This summary is machine-generated.

Radiologists need to understand artificial intelligence (AI) principles for managing AI systems in medical imaging. A task force curated educational resources to aid radiologists in making informed AI purchase decisions.

Keywords:

artificial intelligence deep learning education machine learning radiology

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Artificial Intelligence Approaches to Assessing Primary Cilia

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Published on: May 1, 2021

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Artificial intelligence (AI) is integral to modern medical imaging, impacting image creation, interpretation, and reporting.
Radiologists require a foundational understanding of AI principles for effective system management and procurement.
The increasing integration of AI necessitates targeted educational initiatives for radiology professionals.

Purpose of the Study:

To identify and summarize existing educational materials on artificial intelligence for radiologists.
To support radiologists in responsibly managing AI tools within their practice.
To facilitate informed decision-making regarding the acquisition of AI technologies.

Main Methods:

Formation of a task force by the Radiology Research Alliance (RRA).
Identification and curation of available educational resources relevant to AI in radiology.
Summarization of key principles and materials for a radiology audience.

Main Results:

A curated list of educational materials has been compiled.
The resources cover fundamental AI principles relevant to radiology.
The summary aims to bridge the knowledge gap for radiologists.

Conclusions:

Understanding AI is crucial for radiologists to leverage its benefits and mitigate risks.
The curated educational materials provide a starting point for radiologists' AI literacy.
Informed adoption of AI in radiology depends on accessible and relevant training.