AIMC Topic: Clinical Decision-Making

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CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

Clinical orthopaedics and related research Jul 1, 2021
Humans Artificial Intelligence Machine Learning Clinical Decision-Making Orthopedic Surgeons Deep Learning Image Interpretation, Computer-Assisted Orthopedic Procedures
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Machine Learning: The Next Paradigm Shift in Medical Education.

Academic medicine : journal of the Association of American Medical Colleges Jul 1, 2021
Machine learning (ML) algorithms are powerful prediction tools with immense potential in the clinical setting. There are a number of existing clinical tools that use ML, and many more are in development. Physicians are important stakeholders in the h...
Physician-Patient Relations Liability, Legal COVID-19 Testing Male United States Food and Drug Administration United States History, 20th Century Humans Machine Learning Female Clinical Trials as Topic Diagnostic Imaging Curriculum Diabetic Retinopathy Aged Stakeholder Participation Algorithms Evidence-Based Medicine Education, Medical Physicians Clinical Decision-Making Delivery of Health Care
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How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.

Cardiovascular research Jun 16, 2021
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances ...
Atrial Fibrillation Therapy, Computer-Assisted Risk Assessment Diagnosis, Computer-Assisted Electrocardiography Predictive Value of Tests Translational Research, Biomedical Data Mining Clinical Decision-Making Risk Factors Animals Decision Support Techniques Databases, Factual Prognosis Humans Machine Learning
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Equity and Artificial Intelligence in Surgical Care.

JAMA surgery Jun 1, 2021
Artificial Intelligence Clinical Decision-Making Healthcare Disparities Humans Patient Selection Surgical Procedures, Operative Bias, Implicit
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Artificial Intelligence Opportunities for Vestibular Schwannoma Management Using Image Segmentation and Clinical Decision Tools.

World neurosurgery May 1, 2021
Artificial Intelligence Clinical Decision-Making Image Processing, Computer-Assisted Disease Management Humans Neuroma, Acoustic
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Current trends in the use of machine learning for diagnostics and/or risk stratification in cardiovascular disease.

Cardiovascular research Apr 23, 2021
Artificial Intelligence Prognosis Clinical Decision-Making Reproducibility of Results Risk Factors Cardiovascular Diseases Humans Risk Assessment Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Cardiac Imaging Techniques Predictive Value of Tests
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Opening the black box of AI-Medicine.

Journal of gastroenterology and hepatology Mar 1, 2021
One of the biggest challenges of utilizing artificial intelligence (AI) in medicine is that physicians are reluctant to trust and adopt something that they do not fully understand and regarded as a "black box." Machine Learning (ML) can assist in rea...
Medicine Clinical Decision-Making Artificial Intelligence Humans Machine Learning Algorithms
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DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence.

Nature medicine Feb 1, 2021
Guidelines as Topic Humans Artificial Intelligence Algorithms Clinical Decision-Making
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Machine Learning in Rheumatic Diseases.

Clinical reviews in allergy & immunology Feb 1, 2021
With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex...
Humans Machine Learning Practice Guidelines as Topic Clinical Decision-Making Information Technology Rheumatic Diseases Animals Patient Satisfaction
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Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets.

Lancet (London, England) Jan 16, 2021
BACKGROUND: The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratifica...
Postoperative Complications Datasets as Topic Humans Hemorrhage Mortality Machine Learning Female Acute Coronary Syndrome Clinical Decision-Making Adult Male
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