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Automated Paper Screening for Clinical Reviews Using Large Language Models: Data Analysis Study

February 10, 2024 No Comments

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.

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Apple VR

Apple Vision Pro Initial Perfusionist Review

February 3, 2024 No Comments

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.

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PerfusionGPT

PerfusionGPT Beta Launched on iPerfusion.org

January 26, 2024 No Comments

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.

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ML ECMO

Utilizing Machine Learning to Predict Neurological Injury in Venovenous Extracorporeal Membrane Oxygenation Patients: An Extracorporeal Life Support Organization Registry Analysis

January 12, 2024 No Comments

In a study analyzing 37,473 VV-ECMO patients, machine learning was used to predict acute brain injury (ABI), revealing a 7.1% incidence of ABI and identifying pre-ECMO cardiac arrest as the most significant risk factor. The study’s machine learning models, however, showed sub-optimal performance in predicting ABI, attributed to the low prevalence of ABI and the lack of standardized neuromonitoring and imaging protocols in the ELSO Registry data.

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AT Heart

A Review of Top Cardiology and Cardiovascular Medicine Journal Guidelines regarding the use of Generative Artificial Intelligence Tools in Scientific Writing

January 9, 2024 No Comments

A review of the top 25 cardiology and cardiovascular medicine journals according to the 2023 SCImago rankings revealed that all allow the use of generative AI in scientific writing within ICMJE limitations, but prohibit AI in authorship, image generation, and peer review processes, requiring authors to take full responsibility for their work. These guidelines are standardized and followed by all studied journals, emphasizing the importance of adhering to and updating these policies to uphold scientific integrity.

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Photo Safety GPT

Simulated Cardiopulmonary Bypass: A High Fidelity Model for Developing and Accessing Clinical Perfusion Skills

January 5, 2024 No Comments

The study describes a simulated cardiopulmonary bypass (CPB) model integrated into a mock operative theater, aiming to enhance the training of novice perfusionists. Results from 81 participants indicated high fidelity and effectiveness of the simulation in replicating real CPB scenarios and skills, suggesting its potential for educational use in clinical perfusion training.

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Chip Heart

Validation of an Automated Artificial Intelligence System for 12‑Lead ECG Interpretation

January 3, 2024 No Comments

This study evaluates an AI-powered electrocardiogram (ECG) system with six deep neural networks (DNNs), trained to detect 20 diagnostic patterns in standard 12-lead ECGs, and compares its performance to current computerized interpretation methods. The results show that the AI system outperformed or matched state-of-the-art computerized ECG interpretation in all diagnostic categories, demonstrating its potential as a reliable clinical tool for detecting electrocardiographic abnormalities.

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Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study

December 30, 2023 No Comments

This study compares medical texts written by human experts with those generated by ChatGPT, revealing that while ChatGPT’s texts are fluent, they lack the specific, useful information typically found in human-authored texts. It also presents a machine learning method, with over 95% accuracy, for distinguishing between these two types of texts, underscoring the importance of responsible AI use in medicine.

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Supervised Machine Learning Model to Predict Mortality in Patients Undergoing Venovenous Extracorporeal Membrane Oxygenation from a Nationwide Multicentre Registry

December 29, 2023 No Comments

Machine learning models, specifically extreme gradient boosting and light gradient boosting, demonstrated higher accuracy than conventional models in predicting 90-day mortality for patients undergoing venovenous extracorporeal membrane oxygenation (VV-ECMO). These advanced models, outperforming existing methods like RESP and PRESERVE, show potential for improving patient selection by identifying those less likely to benefit from VV-ECMO therapy.

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