The medical field is experiencing remarkable advancements, notably with the latest technologies-artificial intelligence (AI), big data, high-performance computing (HPC), and high-throughput computing (HTC)-that are in place to offer groundbreaking solutions to support medical professionals in the diagnostic process [...].
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| http://dx.doi.org/10.3390/diagnostics13243671 | DOI Listing |
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Applying artificial intelligence to uncover the genetic landscape of coagulation factors.
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Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Medical Genetics and RNA Biology Unit, Rozzano, Milan, Italy. Electronic address:
Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficiencies. This review introduces AI, with a specific focus on machine learning (ML) methods, and examines its applications in the field of coagulation genetics over the past decade. We observed a significant increase in AI-related publications, with a focus on hemophilia A and B.
View Article and Find Full Text PDFArtificial intelligence terminology, methodology, and critical appraisal: A primer for headache clinicians and researchers.
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Objective: The goal is to provide an overview of artificial intelligence (AI) and machine learning (ML) methodology and appraisal tailored to clinicians and researchers in the headache field to facilitate interdisciplinary communications and research.
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Core Concepts in Pharmacoepidemiology: Principled Use of Artificial Intelligence and Machine Learning in Pharmacoepidemiology and Healthcare Research.
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Artificial intelligence (AI) and machine learning (ML) are important tools across many fields of health and medical research. Pharmacoepidemiologists can bring essential methodological rigor and study design expertise to the design and use of these technologies within healthcare settings. AI/ML-based tools also play a role in pharmacoepidemiology research, as we may apply them to answer our own research questions, take responsibility for evaluating medical devices with AI/ML components, or participate in interdisciplinary research to create new AI/ML algorithms.
View Article and Find Full Text PDFAn analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis.
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University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada. Electronic address:
Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone.
View Article and Find Full Text PDFLearning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.
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Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland.
Introduction: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medicines involved. The label of a medicine is an approved guide that indicates how to prescribe the drug in a safe and effective manner.
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