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Medical imaging (such as X-ray and photography) is a commonly used tool in dermatology [54] and the development of deep learning has been strongly tied to image processing. Therefore, there is a natural fit between the dermatology and deep learning. Machine learning learning holds great potential to process these images for better diagnoses. [55]
Michal Rosen-Zvi (Hebrew: מיכל רוזן-צבי) is an Israeli academic and researcher in the fields of artificial intelligence and deep learning and their applications to healthcare. She is research director for Healthcare Informatics at IBM Research in Haifa. [1]
Fakhreddine (Fakhri) Karray is a Tunisian-Canadian artificial intelligence scientist, electrical and computer engineer, author, and academic.He served as the Loblaws Research Chair of Artificial Intelligence at the University of Waterloo's (UWaterloo) Department of Electrical and Computer Engineering, and as the inaugural co-director of the Waterloo AI Institute at UWaterloo. [1]
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
HRHIS is a human resource for health information system for management of human resources for health developed by University of Dar es Salaam college of information and communication technology, Department of Computer Science and Engineering, for Ministry of Health and Social Welfare (Tanzania) and funded by the Japan International Cooperation ...
Parallel intelligence has gained considerable attention in recent years due to advancements in AI technologies, such as machine learning, deep learning, and natural language processing. These technologies have enabled the development of intelligent systems that can collaborate with humans in various domains, including healthcare, finance ...
The MIT Abdul Latif Jameel Clinic for Machine Learning in Health (commonly, MIT Jameel Clinic; previously, J-Clinic) is a research center at the Massachusetts Institute of Technology (MIT) in the field of artificial intelligence (AI) and health sciences, including disease detection, drug discovery, and the development of medical devices.
GNoME employs deep learning techniques to efficiently explore potential material structures, achieving a significant increase in the identification of stable inorganic crystal structures. The system's predictions were validated through autonomous robotic experiments, demonstrating a noteworthy success rate of 71%.