In the OR, precision and focus can mean the difference between life and death. However, surgical patient outcomes hinge on more than the competence of those working in these inherently intense environments. Every procedure also depends on the laborious, behind-the-scenes efforts of the people responsible for ensuring every surgical instrument…

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Editor's Note Recent reporting from Fierce Healthcare highlights two notable technology advances that promise to impact surgical patients and caregivers:  an AI-driven blood pressure monitoring system and a remote-controlled endovascular robotics platform. The first development, from BD, is designed to help clinicians anticipate and manage dangerous blood pressure drops during…

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Editor's Note Machine learning (ML) models designed to predict patient mortality are falling short when it comes to identifying severe injuries that could lead to death, according to a March 27 report in TechTarget. The article focuses on research published in Nature Communications Medicine found that ML mortality prediction models…

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Editor's Note A newly developed predictive model offers healthcare professionals a dynamic tool to assess the risk of nosocomial infections (NIs) in patients following colon cancer surgery, potentially improving early intervention strategies. Published February 27 in Frontiers in Oncology, the study introduces a nomogram—a statistical model that visualizes key risk…

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Editor’s Note Large language models (LLMs) outperformed traditional methods in predicting postoperative complications, according to a study on artificial intelligence (AI) in perioperative risk assessment published February 11 in the journal Nature. Results indicate AI-driven models could enhance patient safety and streamline clinical workflows by detecting complications earlier. Researchers analyzed…

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Editor's Note Although the role of artificial intelligence is still evolving, early applications show significant potential to address challenges with burnout, staffing shortages, and high patient-to-nurse ratios, according to a January 10 article in Medscape. Offering a broad overview of the state of AI in nursing, complete with expert testimony…

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Editor's Note A recent study developed and validated an artificial intelligence (AI) model leveraging natural language processing (NLP) and machine learning to significantly improve prediction accuracy for surgical case length. Published November 29 in the journal Surgery, the findings show promise for using AI as an alternative to current methods…

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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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