Medicine is one of the most sensitive fields in which artificial intelligence (AI) is extensively used, spanning from medical image analysis to clinical support. Specifically, in medicine, where every decision may severely affect human lives, the iss...
BACKGROUND: Artificial intelligence (AI) is rapidly advancing in health care, particularly in medical imaging, offering potential for improved efficiency and reduced workload. However, there is little systematic evidence on process factors for succes...
Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the necessity for prompt and precise diagnosis to mitigate consequences and healthcare expenditures. This work presents a complete hybrid methodology that...
Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning automates this task effectively, it struggles in ultra low-data regimes for the scarcity of annotated segmentation m...
BMC medical informatics and decision making
Jul 6, 2025
BACKGROUND: The need for better AI models fuels the demand for larger and larger high-quality datasets with significant diversity. Over the years, many medical imaging datasets have been published globally, but existing datasets do not contain enough...
Medical image encryption is important for maintaining the confidentiality of sensitive medical data and protecting patient privacy. Contemporary healthcare systems store significant patient data in text and graphic form. This research proposes a New ...
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Jun 19, 2025
Deep generative models (DGMs) have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restor...
Medical image reconstruction aims to generate high-quality images from incompletely sampled raw sensor data, which poses an ill-posed inverse problem. Traditional iterative reconstruction methods rely on prior information to empirically construct reg...
BACKGROUND: Hospital diagnostic imaging significantly contributes to healthcare's carbon emissions, with modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), accounting for a disproportionate share of energy consumption a...
Journal of nuclear medicine technology
Jun 4, 2025
Disparity among gender and ethnicity remains an issue across medicine and health science. Only 26%-35% of trainee radiologists are female, despite more than 50% of medical students' being female. Similar gender disparities are evident across the medi...
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