AIMC Topic: Radiology
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Visualizing radiological data bias through persistence images.
Oncotarget
Nov 12, 2024
Persistence images, derived from topological data analysis, emerge as a powerful tool for visualizing and mitigating biases in radiological data interpretation and AI model development. This technique transforms complex topological features into stab...
Testing process for artificial intelligence applications in radiology practice.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Nov 9, 2024
Artificial intelligence (AI) applications are becoming increasingly common in radiology. However, ensuring reliable operation and expected clinical benefits remains a challenge. A systematic testing process aims to facilitate clinical deployment by c...
Performance of Multimodal Large Language Models in Japanese Diagnostic Radiology Board Examinations (2021-2023).
Academic radiology
Nov 8, 2024
RATIONALE AND OBJECTIVES: To evaluate the performance of various multimodal large language models (LLMs) in the Japanese Diagnostic Radiology Board Examinations (JDRBE) both with and without images.
Collaboration between clinicians and vision-language models in radiology report generation.
Nature medicine
Nov 7, 2024
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
Nov 7, 2024
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...
Deployment of Artificial Intelligence in Radiology: Strategies for Success.
AJR. American journal of roentgenology
Oct 30, 2024
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 ...
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)
Oct 30, 2024
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)
Oct 24, 2024
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
Oct 23, 2024
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
Oct 21, 2024
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...
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