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May 2024
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FDA clears Heartvista’s AI-assisted cardiac MRI tool

Editor's Note Heartvista (Los Altos, California) has obtained clearance from the Food and Drug Administration (FDA) for its artificial intelligence (AI)-assisted One Click autonomous MRI acquisition software for cardiac ischemia exams, the October 30 BioWorld MedTech reports. Integrated with existing MRI scanners, the software uses AI to guide image acquisition,…

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By: Judy Mathias
November 5, 2019
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AI outperforms clinicians’ judgment in triaging postop patients to ICU

Editor's Note Artificial intelligence (AI) in the form of a machine-learned algorithm correctly triaged the vast majority of postoperative patients to the ICU, in this pilot study presented October 29 at the American College of Surgeons Clinical Congress 2019 in San Francisco. The algorithm included 87 clinical variables and 15…

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By: Judy Mathias
October 30, 2019
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Can machine learning predict 6-month mortality in cancer patients?

Editor's Note In this study, machine learning algorithms accurately identified cancer patients who were at risk of 6-month mortality. Of 26,525 cancer patients analyzed, machine learning models based on structured electronic health record data accurately predicted short-term mortality risk with good discrimination and positive predictive value. When the gradient boosting…

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By: Judy Mathias
October 28, 2019
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Radiologist, AI combination improves breast cancer detection

Editor's Note An artificial intelligence (AI) model was able to detect breast cancer with nearly 90% accuracy when combined with analysis from radiologists, the October 18 Health IT Analytics reports. Researchers from New York University Schools of Medicine and Data Science developed the model to first consider small patches of…

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By: Judy Mathias
October 21, 2019
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Identifying postop complications using EHR data and machine learning

Editor's Note Using machine learning on electronic health record (EHR) postoperative data linked to the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) outcomes data, researchers developed a model with 163 predictors of postoperative complications at the University of Colorado Hospital. Of 6,840 patients analyzed with the…

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By: Judy Mathias
October 10, 2019
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Will the success of AI in healthcare depend on nurses?

Editor's Note Nurses have always played an important role when new health information technology (IT) has been implemented, and the emergence of artificial intelligence (AI) and the changes it can bring to healthcare will be no exception, reports the September 27 HealthcareITNews. Though some nurses may view AI as another…

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By: Judy Mathias
October 7, 2019
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AI detects key findings in chest x-rays of pneumonia patients within 10 seconds

Editor's Note Researchers from Intermountain Healthcare and Stanford University say 10 seconds is how long it takes for their new model, which uses artificial intelligence (AI), to accurately identify key findings in chest x-rays of patients in the emergency department suspected of having pneumonia. The researchers presented the findings of…

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By: Judy Mathias
September 30, 2019
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AI predicts radiation therapy side effects for head and neck cancer patients

Editor's Note A computer model, which for the first time accurately predicted two of the most challenging side effects of radiation therapy for head and neck cancer patients, was presented September 26 at the Annual Meeting of the American Society for Radiation Oncology in Chicago. Researchers from the University of…

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By: Judy Mathias
September 30, 2019
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Using AI to improve OR efficiency

Editor's Note In this study, a machine learning algorithm resulted in the most accurate estimation of operative case-time duration. Researcher developed models to predict case-time duration using linear regression and supervised machine learning. Each model included an all-inclusive model, service-specific models, and surgeon-specific models. The data set used included 46,986…

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By: Judy Mathias
September 26, 2019
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Study: AI equal to humans in medical diagnosis

Editor's Note When deep learning algorithms were compared with health-care professionals in classifying diseases using medical imaging, diagnostic performance was equivalent between the two. In this meta-analysis of 14 studies, researchers found that deep learning systems correctly detected a disease state 87% of the time, compared with 86.4% for healthcare…

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By: Judy Mathias
September 25, 2019
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