AIMC Topic: Diagnostic Imaging

Clear Filters Showing 81 to 90 of 978 articles

Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical Imaging, Part 1: Preliminary Evaluation.

Journal of nuclear medicine technology
Generative artificial intelligence (AI) text-to-image production could reinforce or amplify gender and ethnicity biases. Several text-to-image generative AI tools are used for producing images that represent the medical imaging professions. White mal...

A review of convolutional neural network based methods for medical image classification.

Computers in biology and medicine
This study systematically reviews CNN-based medical image classification methods. We surveyed 149 of the latest and most important papers published to date and conducted an in-depth analysis of the methods used therein. Based on the selected literatu...

AI-powered techniques in anatomical imaging: Impacts on veterinary diagnostics and surgery.

Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft
BACKGROUND: Artificial intelligence (AI) is rapidly transforming veterinary diagnostic imaging, offering improved accuracy, speed, and efficiency in analyzing complex anatomical structures. AI-powered systems, including deep learning and convolutiona...

The translation of in-house imaging AI research into a medical device ensuring ethical and regulatory integrity.

European journal of radiology
This manuscript delineates the pathway from in-house research on Artificial Intelligence (AI) to the development of a medical device, addressing critical phases including conceptualization, development, validation, and regulatory compliance. Key stag...

Virtual histopathology methods in medical imaging - a systematic review.

BMC medical imaging
Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, oft...

The risk of shortcutting in deep learning algorithms for medical imaging research.

Scientific reports
While deep learning (DL) offers the compelling ability to detect details beyond human vision, its black-box nature makes it prone to misinterpretation. A key problem is algorithmic shortcutting, where DL models inform their predictions with patterns ...

MedSegBench: A comprehensive benchmark for medical image segmentation in diverse data modalities.

Scientific data
MedSegBench is a comprehensive benchmark designed to evaluate deep learning models for medical image segmentation across a wide range of modalities. It covers a wide range of modalities, including 35 datasets with over 60,000 images from ultrasound, ...

Lessons on AI implementation from senior clinical practitioners: An exploratory qualitative study in medical imaging and radiotherapy in the UK.

Journal of medical imaging and radiation sciences
INTRODUCTION: Artificial Intelligence (AI) has the potential to transform medical imaging and radiotherapy; both fields where radiographers' use of AI tools is increasing. This study aimed to explore the views of those professionals who are now using...

Manual data labeling, radiology, and artificial intelligence: It is a dirty job, but someone has to do it.

Magnetic resonance imaging
In this letter to the editor, authors highlight the key role of data labeling in training AI models for medical imaging, discussing the complexities, resource demands, costs, and the relevance of quality control in the labeling process including the ...

Cultivating diagnostic clarity: The importance of reporting artificial intelligence confidence levels in radiologic diagnoses.

Clinical imaging
Accurate image interpretation is essential in the field of radiology to the healthcare team in order to provide optimal patient care. This article discusses the use of artificial intelligence (AI) confidence levels to enhance the accuracy and dependa...