International Perfusion Association
PerfusionGPT is an AI-powered chatbot based on Open-AI’s latest models, specifically designed to provide expert knowledge for perfusionists in cardiac surgery. It serves as a critical resource for both clinical decision-making and educational purposes in the field of perfusion.
PerfusionGPT is an AI-powered chatbot based on ChatGPT-4, specifically designed to provide expert knowledge for perfusionists in cardiac surgery. It serves as a critical resource for both clinical decision-making and educational purposes in the field of perfusion.
This study explores the efficacy of using artificial intelligence (autoMAPSE) with transesophageal echocardiography (TEE) to continuously monitor left ventricular (LV) function in postoperative intensive care patients. The prospective observational study involved 50 patients, monitored for 120 minutes post-cardiac surgery. Results showed that autoMAPSE provided precise, low-bias, and concordant measurements compared to manual methods, demonstrating excellent feasibility and trending ability.
This study developed a predictive model for perioperative myocardial injury (PMI) using hybrid feature selection (FS) methods in patients undergoing noncoronary cardiac surgery with cardiopulmonary bypass (CPB). Conducted at Fuwai Hospital, China, the retrospective study included 1130 patients, with an overall PMI incidence of 20.3%. Various machine learning models were evaluated, with the Naïve Bayes model achieving the highest AUC. The study highlighted the importance of factors like prolonged CPB, aortic clamp time, and preoperative low platelet count in predicting PMI risk.
This systematic review assesses clinical prediction models (CPMs) that incorporate intra-operative variables to predict outcomes following adult cardiac surgery. It highlights the identification of 24 CPMs, predominantly predicting acute kidney injury and peri-operative mortality, using common variables like cardiopulmonary bypass time. Despite acceptable discrimination in internally validated models, poor calibration and high bias risk limit their practical use. The review suggests potential improvement in model accuracy with intra-operative data, advocating for more robust studies.
Critical care medicine has made significant advancements in the 21st century, notably improving patient outcomes in ICUs. Innovations such as Precision Medicine, Telemedicine, AI-driven tools, advanced Organ Support, new Infection Control tactics, refined Ventilation Strategies, and enhanced Sepsis Management reflect a dynamic landscape. These developments prioritize technology, research, and patient-centered approaches, showcasing a promising future for addressing modern medical challenges.
This article explores the role of artificial intelligence, specifically ChatGPT and generative pre-trained transformers, in cardiac surgery and heart transplantation. It discusses the potential benefits of AI in enhancing clinical care, decision-making, training, research, and education. However, it also cautions against risks related to validation, ethical challenges, and medicolegal concerns. ChatGPT is presented as a tool to support surgeons, not replace them, emphasizing the importance of human oversight and the nuanced understanding of patient-specific circumstances.
The integration of Artificial Intelligence (AI) into Transcatheter Aortic Valve Replacement (TAVR) is revolutionizing cardiology, offering enhanced patient selection, procedural planning, and post-implantation monitoring. As TAVR becomes a viable option for a broader range of patients with severe aortic stenosis, AI’s role in interpreting medical imaging and developing risk models is increasingly critical. This article delves into AI’s current contributions to TAVR and examines the challenges and future directions of its implementation in ensuring optimized patient outcomes.
This study evaluates the effectiveness of OpenAI’s GPT and GPT-4 in streamlining the systematic review process of clinical research papers. By automating the screening of titles and abstracts against human benchmarks, the models demonstrated high accuracy and efficiency, with an accuracy of 0.91 and a macro F1-score of 0.60. The comparison with human reviewers showed a significant reduction in time and effort, highlighting the models’ potential to improve the quality and reliability of clinical reviews. The findings suggest that GPT models can serve as valuable aids in medical research, enhancing both the speed and accuracy of literature screening.
The Apple Vision Pro, with its advanced visual and audio capabilities, offers potential applications in the medical field, including enhanced training, therapeutic tools, and surgical assistance. However, its high cost and potential discomfort during extended use are significant considerations that may limit its widespread adoption in healthcare settings.
