Editor's Note AI and machine learning (ML) models show significant promise in enhancing preoperative estimates of surgical control time (SCT), which are frequently wrong, according to a study published September 10 in Perioperative Care and Operating Room Management. The longitudinal study examined differences between predicted and actual SCTs, broken down…

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Editor's Note An algorithm developed by researchers at Duke University Medical Centre is designed to assist patients diagnosed with intermediate-stage liver cancer in making decisions about surgery, according to an October 9 report in News Medical Life Sciences. Known as the Modified Metroticket, this tool predicts overall survival and recurrence-free…

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Editor's Note Researchers at the Medical University of Vienna developed a new method using the Temporal Fusion Transformer (TFT) model to predict intraoperative hypotension in patients under general anesthesia. According to findings published August 30 in eClinical Medicine, part of The Lancet, the model utilizes routine vital sign data, including…

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When it comes to the adoption of artificial intelligence (AI) in medicine, radiology is leading the charge. As of May 13, 2024, the US Food and Drug Administration (FDA) had approved nearly 900 AI- and machine learning (ML)-enabled devices, and the vast majority of them are in radiology. One example…

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Editor's Note An AI prediction model that uses near-real-time data to generate a patient risk score shows the promise of AI for helping physicians and nurses coordinate on patient care, according to findings published March 25 in JAMA Internal Medicine. Performed by researchers at Stanford Medicine, the study examined an…

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Editor's Note Artificial intelligence (AI) is a useful tool for helping clinicians to determine health problems from medical imaging, but AI often provides just one answer, when there may be a number of possible interpretations. Now, researchers from MIT, the Broad Institute of MIT and Harvard, and Massachusetts General Hospital…

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Editor's Note Reducing surgeon manipulation of electronic health record (EHR) OR scheduling systems can improve efficiency, save resources, and enhance service to patients, according to data published in the March/April issue of the Journal of Healthcare Management. Although predictive models using EHR and machine learning improve accuracy compared to traditional…

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Editor's Note: Artificial intelligence (AI) leveraging machine learning (ML) and natural language processing (NLP, a subset of machine learning) models can help identify donors with kidneys unsuitable for organ transplant, according to a study published November 1 in Jama Surgery. Despite the unmet need, many deceased-donor kidneys are discarded or…

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ORs are a key revenue driver for hospitals, with surgical services accounting for nearly half of hospital margins. Efficient use of OR time is therefore critical to sustaining and growing hospital revenue and profitability. Perioperative leaders face an ongoing challenge in optimizing OR utilization, however, because of the common mindset…

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Editor's Note An artificial intelligence (AI) tool called CLOT (Children’s Likelihood of Thrombosis) developed at Vanderbilt University Medical Center accurately identified pediatric patients at high risk for blood clots in a clinical trial. The findings were reported in JAMA Network on October 13. The researchers analyzed the electronic health records…

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