Journal of cardiothoracic and vascular anesthesia
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J. Cardiothorac. Vasc. Anesth. · May 2024
ReviewArtificial Intelligence for Anesthesiology Board-Style Examination Questions: Role of Large Language Models.
New artificial intelligence tools have been developed that have implications for medical usage. Large language models (LLMs), such as the widely used ChatGPT developed by OpenAI, have not been explored in the context of anesthesiology education. Understanding the reliability of various publicly available LLMs for medical specialties could offer insight into their understanding of the physiology, pharmacology, and practical applications of anesthesiology. ⋯ GPT-4 significantly outperformed GPT-3.5 and Bard in terms of overall performance, but lacked consistency in providing explanations that aligned with scientific and medical consensus. Although GPT-4 shows promise, current LLMs are not sufficiently advanced to answer anesthesiology board examination questions with passing success. Further iterations and domain-specific training may enhance their utility in medical education.
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J. Cardiothorac. Vasc. Anesth. · May 2024
ReviewHow Would I Treat My Own Thoracoabdominal Aortic Aneurysm: Perioperative Considerations From the Anesthesiologist Perspective.
A thoracoabdominal aortic aneurysm (TAAA) can be potentially life-threatening due to its associated risk of rupture. Thoracoabdominal aortic aneurysm repair, performed as endovascular repair and/or open surgery, is the recommended therapy of choice. ⋯ Therefore, preoperative risk assessment and intraoperative anesthesia management addressing these potential hazards are essential to improving patients' outcomes. Based on a presented index case, an overview focusing on anesthetic measures to identify perioperatively and manage these risks in TAAA repair is provided.
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J. Cardiothorac. Vasc. Anesth. · May 2024
ReviewOverview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology.
Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. ⋯ To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.