AIMC Topic: Radiology

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Patients' Attitudes Toward the Use of Artificial Intelligence as a Diagnostic Tool in Radiology in Saudi Arabia: Cross-Sectional Study.

JMIR human factors
BACKGROUND: Artificial intelligence (AI) is widely used in various medical fields, including diagnostic radiology as a tool for greater efficiency, precision, and accuracy. The integration of AI as a radiological diagnostic tool has the potential to ...

Transforming Health Care Landscapes: The Lever of Radiology Research and Innovation on Emerging Markets Poised for Aggressive Growth.

Journal of the American College of Radiology : JACR
Advances in radiology are crucial not only to the future of the field but to medicine as a whole. Here, we present three emerging areas of medicine that are poised to change how health care is delivered-hospital at home, artificial intelligence, and ...

Pitfalls in Interpretive Applications of Artificial Intelligence in Radiology.

AJR. American journal of roentgenology
Interpretive artificial intelligence (AI) tools are poised to change the future of radiology. However, certain pitfalls may pose particular challenges for optimal AI interpretative performance. These include anatomic variants, age-related changes, po...

The Picasso's skepticism on computer science and the dawn of generative AI: questions after the answers to keep "machines-in-the-loop".

European radiology experimental
Starting from Picasso's quote ("Computers are useless. They can only give you answers"), we discuss the introduction of generative artificial intelligence (AI), including generative adversarial networks (GANs) and transformer-based architectures such...

United States newspaper and online media coverage of artificial intelligence and radiology from 1998 to 2023.

Clinical imaging
OBJECTIVE: To evaluate the frequency and content of media coverage pertaining to artificial intelligence (AI) and radiology in the United States from 1998 to 2023.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.

BMC medical education
BACKGROUND: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students.