AIMC Topic: Diagnostic Imaging
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For the busy clinical-imaging professional in an AI world: Gaining intuition about deep learning without math.
Journal of medical imaging and radiation sciences
Oct 21, 2024
Medical diagnostics comprise recognizing patterns in images, tissue slides, and symptoms. Deep learning algorithms (DLs) are well suited to such tasks, but they are black boxes in various ways. To explain DL Computer-Aided Diagnostic (CAD) results an...
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...
AI in radiology: From promise to practice - A guide to effective integration.
European journal of radiology
Oct 20, 2024
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 ...
A systematic review of generalization research in medical image classification.
Computers in biology and medicine
Oct 20, 2024
Numerous Deep Learning (DL) classification models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and impleme...
Medical imaging and radiation science students' use of artificial intelligence for learning and assessment.
Radiography (London, England : 1995)
Oct 19, 2024
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 review of AutoML optimization techniques for medical image applications.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oct 16, 2024
Automatic analysis of medical images using machine learning techniques has gained significant importance over the years. A large number of approaches have been proposed for solving different medical image analysis tasks using machine learning and dee...
Early cancer detection using deep learning and medical imaging: A survey.
Critical reviews in oncology/hematology
Oct 15, 2024
Cancer, characterized by the uncontrolled division of abnormal cells that harm body tissues, necessitates early detection for effective treatment. Medical imaging is crucial for identifying various cancers, yet its manual interpretation by radiologis...
Continual learning in medical image analysis: A survey.
Computers in biology and medicine
Sep 26, 2024
In the dynamic realm of practical clinical scenarios, Continual Learning (CL) has gained increasing interest in medical image analysis due to its potential to address major challenges associated with data privacy, model adaptability, memory inefficie...
The knowledge and perception of patients in Malta towards artificial intelligence in medical imaging.
Journal of medical imaging and radiation sciences
Sep 23, 2024
INTRODUCTION: Artificial intelligence (AI) is becoming increasingly implemented in radiology, especially in image reporting. Patients' perceptions about AI integration in medical imaging is a relatively unexplored area that has received limited inves...
Opportunity and Opportunism in Artificial Intelligence-Powered Data Extraction: A Value-Centered Approach.
AJR. American journal of roentgenology
Sep 18, 2024
Radiologists' traditional role in the diagnostic process is to respond to specific clinical questions and reduce uncertainty enough to permit treatment decisions to be made. This charge is rapidly evolving due to forces such as artificial intelligenc...
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