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Radiology

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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...

Collaboration between clinicians and vision-language models in radiology report generation.

Nature medicine
Automated radiology report generation has the potential to improve patient care and reduce the workload of radiologists. However, the path toward real-world adoption has been stymied by the challenge of evaluating the clinical quality of artificial i...

ChatGPT and radiology report: potential applications and limitations.

La Radiologia medica
Large language models like ChatGPT, with their growing accessibility, are attracting increasing interest within the artificial intelligence medical field, particularly in the analysis of radiology reports. These present a valuable opportunity to expl...

Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.

Korean journal of radiology
Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data for AI modeling in various medical fields. Image-generative AI can produce large amounts of unannotat...

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,...

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 ...

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...

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...

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...

Deployment of Artificial Intelligence in Radiology: Strategies for Success.

AJR. American journal of roentgenology
Radiology, as a highly technical and information-rich medical specialty, is well suited for artificial intelligence (AI) product development, and many U.S. FDA-cleared AI medical devices are authorized for uses within the specialty. In this Clinical ...