AIMC Topic: Radiology

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Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions.

Diagnostic and interventional radiology (Ankara, Turkey)
With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To ado...

Approaches and Limitations of Machine Learning for Synthetic Ultrasound Generation: A Scoping Review.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
This scoping review examines the emerging field of synthetic ultrasound generation using machine learning (ML) models in radiology. Nineteen studies were analyzed, revealing three primary methodological strategies: unconditional generation, condition...

Artificial Intelligence to Improve Patient Understanding of Radiology Reports.

The Yale journal of biology and medicine
Diagnostic imaging reports are generally written with a target audience of other providers. As a result, the reports are written with medical jargon and technical detail to ensure accurate communication. With implementation of the 21st Century Cures ...

AC-Faster R-CNN: an improved detection architecture with high precision and sensitivity for abnormality in spine x-ray images.

Physics in medicine and biology
In clinical medicine, localization and identification of disease on spinal radiographs are difficult and require a high level of expertise in the radiological discipline and extensive clinical experience. The model based on deep learning acquires cer...

Assessing AI-Powered Patient Education: A Case Study in Radiology.

Academic radiology
RATIONALE AND OBJECTIVES: With recent advancements in the power and accessibility of artificial intelligence (AI) Large Language Models (LLMs) patients might increasingly turn to these platforms to answer questions regarding radiologic examinations a...

AI pitfalls and what not to do: mitigating bias in AI.

The British journal of radiology
Various forms of artificial intelligence (AI) applications are being deployed and used in many healthcare systems. As the use of these applications increases, we are learning the failures of these models and how they can perpetuate bias. With these n...

AI in medical imaging grand challenges: translation from competition to research benefit and patient care.

The British journal of radiology
Artificial intelligence (AI), in one form or another, has been a part of medical imaging for decades. The recent evolution of AI into approaches such as deep learning has dramatically accelerated the application of AI across a wide range of radiologi...