AIMC Topic: Radiology

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Radiographer Education and Learning in Artificial Intelligence (REAL-AI): A survey of radiographers, radiologists, and students' knowledge of and attitude to education on AI.

Radiography (London, England : 1995)
INTRODUCTION: In Autumn 2023, amendments to the Health and Care Professions Councils (HCPC) Standards of Proficiency for Radiographers were introduced requiring clinicians to demonstrate awareness of the principles of AI and deep learning technology,...

Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development.

Radiography (London, England : 1995)
OBJECTIVES: Artificial intelligence (AI) is rapidly being integrated into medical imaging practice, prompting calls to enhance AI education in undergraduate radiography programs. Combining evidence from literature, practitioner insights, and industry...

Large language models for structured reporting in radiology: past, present, and future.

European radiology
Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adopti...

Current Radiology workforce perspective on the integration of artificial intelligence in clinical practice: A systematic review.

Journal of medical imaging and radiation sciences
INTRODUCTION: Artificial Intelligence (AI) represents the application of computer systems to tasks traditionally performed by humans. The medical imaging profession has experienced a transformative shift through the integration of AI. While there hav...

AI in radiology: From promise to practice - A guide to effective integration.

European journal of radiology
While Artificial Intelligence (AI) has the potential to transform the field of diagnostic radiology, important obstacles still inhibit its integration into clinical environments. Foremost among them is the inability to integrate clinical information ...

Medical imaging and radiation science students' use of artificial intelligence for learning and assessment.

Radiography (London, England : 1995)
INTRODUCTION: Artificial intelligence has permeated all aspects of our existence, and medical imaging has shown the burgeoning use of artificial intelligence in clinical environments. However, there are limited empirical studies on radiography studen...

A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult.

Journal of imaging informatics in medicine
This study aims to investigate whether global mammographic radiomic features (GMRFs) can distinguish hardest- from easiest-to-interpret normal cases for radiology trainees (RTs). Data from 137 RTs were analysed, with each interpreting seven education...

Large Language Models can Help with Biostatistics and Coding Needed in Radiology Research.

Academic radiology
INTRODUCTION: Original research in radiology often involves handling large datasets, data manipulation, statistical tests, and coding. Recent studies show that large language models (LLMs) can solve bioinformatics tasks, suggesting their potential in...

Paranoid About Androids: A Review of Robotics in Radiology.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
In tandem with the ever-increasing global population, the demand for diagnostic radiology service provision is on the rise and at a disproportionate rate compared to the number of radiologists available to practice. The current "revolution in robotic...

Exploring the integration of artificial intelligence in radiology education: A scoping review.

Current problems in diagnostic radiology
BACKGROUND: The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and practice within the field. This scoping review aims to systematically explore and map the current lands...