Medical imaging technologies diagnostics, and AI innovations enhance patient care through advanced imaging techniques.
We are Machine Learning in Medical Imaging and Diagnostics, an interdisciplinary Research Lab based in Dublin. We investigate novel machine learning concepts in the medical field.Our research is supported by funding from Enterprise Ireland (EI), Science Foundation Ireland (SFI), Health Research Board...
Medical imaging and diagnostics have benefited from recent advances in machine learning in general and deep learning in particular. There are a large number of studies that report significant...doi:10.1007/978-3-030-58080-3_293-1Syed Muhammad Anwar...
Medical imaging has transformed clinical diagnostics. Here, authors present RadDiag, a foundational model for comprehensive disease diagnosis using multi-modal inputs, demonstrating superior zero-shot performance on external datasets compared to other foundation models and showing broad applicability across va...
Esaote is one of the world’s leading producers of medical diagnostic imaging systems: 40 years of innovation in healthcare to improve quality of life
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020....
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Over the last five years, advances in imaging have revolutionized almost every aspect of medicine. Medical imaging is a core function in the diagnostics industry that helps medical, healthcare professionals to identify and analyze medical problems that are based on different imaging modalities. “...
2D, 3D, and multimodal imaging have unlocked an array of applications to systematically address complex problems in many areas of research such as drug monitoring, natural products forensics, and cancer diagnostics. In the present review,... CJ Perez,AK Bagga,SS Prova,... - 《Rapid Communicatio...
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted