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Radiological Investigation I: X-ray and CT

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...

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Updated: Nov 7, 2025

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

The Case for User-Centered Artificial Intelligence in Radiology.

Ross W Filice¹, Raj M Ratwani¹

¹MedStar Health, MedStar Georgetown University Hospital, 3800 Reservoir Rd, NW CG201, Washington DC, 20007 (R.W.F.); and MedStar Health, National Center for Human Factors in Healthcare, Washington, DC (R.M.R.).

Radiology. Artificial Intelligence

|May 3, 2021

Summary

This summary is machine-generated.

User-centered design and human factors science are crucial for successful artificial intelligence adoption in radiology, drawing lessons from past technology transitions.

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Area of Science:

Radiology
Artificial Intelligence
Human Factors Science

Background:

Past technology transitions highlight the significance of user-centered design.
Understanding human factors is essential for successful technology implementation.

Discussion:

User-centered design principles must guide the integration of artificial intelligence in radiology.
Human factors science provides a framework for optimizing AI tools for radiologists.

Key Insights:

AI in radiology requires a strong focus on the end-user experience.
Human-centered approaches are vital for overcoming adoption barriers in medical AI.

Outlook:

Future AI development in radiology must prioritize usability and workflow integration.
Successful AI implementation hinges on a deep understanding of human-computer interaction.