AIMC Topic: Radiology

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Patient-centered research in radiology: A Canadian perspective.

Clinical imaging
This article examines the importance of patient-centered research in radiology with an emphasis on incorporating the patient perspective to improve patient-reported outcomes (PROs) and research relevance. The methods for effective patient engagement ...

Is a score enough? Pitfalls and solutions for AI severity scores.

European radiology experimental
Severity scores, which often refer to the likelihood or probability of a pathology, are commonly provided by artificial intelligence (AI) tools in radiology. However, little attention has been given to the use of these AI scores, and there is a lack ...

Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology.

Scientific reports
Deep learning models require large amounts of annotated data, which are hard to obtain in the medical field, as the annotation process is laborious and depends on expert knowledge. This data scarcity hinders a model's ability to generalise effectivel...

Patient perspectives on AI in radiology: Insights from the United Arab Emirates.

Clinical imaging
RATIONALE AND OBJECTIVES: Artificial intelligence (AI) enhances diagnostic accuracy, efficiency, and patient outcomes in radiology. Patient acceptance is essential for successful integration. This study examines patient perspectives on AI in radiolog...

Curriculum check, 2025-equipping radiology residents for AI challenges of tomorrow.

Abdominal radiology (New York)
The exponential rise in the artificial intelligence (AI) tools for medical imaging is profoundly impacting the practice of radiology. With over 1000 FDA-cleared AI algorithms now approved for clinical use-many of them designed for radiologic tasks-th...

Regulating Generative AI in Radiology Practice: A Trilaminar Approach to Balancing Risk with Innovation.

Academic radiology
Generative AI tools have proliferated across the market, garnered significant media attention, and increasingly found incorporation into the radiology practice setting. However, they raise a number of unanswered questions concerning governance and ap...

Revolutionizing radiology education: exploring innovative teaching methods.

Abdominal radiology (New York)
The field of radiology education is undergoing a paradigm shift due to technological advancements and the increasing complexity of medical imaging. Traditional didactic teaching methods are progressively being supplemented or replaced by innovative p...

A Comparative Bicentric Study on Ultrasound Education for Students: App- and AI-Supported Learning Versus Traditional Hands-on Instruction (AI-Teach Study).

Academic radiology
BACKGROUND: The integration of artificial intelligence (AI) into medical education presents significant opportunities for enhancing teaching methods and student learning outcomes. Despite its potential benefits, the implementation of AI in curricula ...

Integrating AI into medical imaging curricula: Insights from UK HEIs.

Radiography (London, England : 1995)
INTRODUCTION: With artificial intelligence (AI) becoming increasingly integrated into medical imaging, the Health and Care Professions Council (HCPC) updated its Standards of Proficiency for Radiographers in Autumn 2023. These changes require clinici...

AI as teacher: effectiveness of an AI-based training module to improve trainee pediatric fracture detection.

Skeletal radiology
OBJECTIVE: Prior work has demonstrated that AI access can help residents more accurately detect pediatric fractures. We wished to evaluate the effectiveness of an unsupervised AI-based training module as a pediatric fracture detection educational too...