AI Medical Compendium Topic

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Accuracy of LLMs in medical education: evidence from a concordance test with medical teacher.

BMC medical education
BACKGROUND: There is an unprecedented increase in the use of Generative AI in medical education. There is a need to assess these models' accuracy to ensure patient safety. This study assesses the accuracy of ChatGPT, Gemini, and Copilot in answering ...

O blood usage trends in the pediatric population 2015-2019: A multi-institutional analysis.

Transfusion
BACKGROUND: In 2019, AABB released the bulletin "Recommendations on the Use of Group O Red Blood Cells" in which the recommendations about pediatric and neonatal blood transfusions were limited. Eight U.S. pediatric hospitals sought to determine tren...

Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning.

Geospatial health
INTRODUCTION: Chronic Obstructive Pulmonary Disease (COPD) mortality rates and global warming have been in the focus of scientists and policymakers in the past decade. The long-term shifts in temperature and weather patterns, commonly referred to as ...

Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post-COVID-19 Condition: Protocol for an Observational Study.

JMIR research protocols
BACKGROUND: Post-COVID-19 condition is emerging as a new epidemic, characterized by the persistence of COVID-19 symptoms beyond 3 months, and is anticipated to substantially alter the lives of millions of people globally. Patients with severe episode...

Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.

BMJ open quality
OBJECTIVE: Substance use disorder (SUD) is clinically under-detected and under-documented. We built and validated machine learning (ML) models to estimate SUD prevalence from electronic health record (EHR) data and to assess variation in facility-lev...

Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy.

International journal of cardiology
BACKGROUND: Takotsubo cardiomyopathy (TC) is an acute heart failure syndrome characterized by transient left ventricular dysfunction, often triggered by stress. Data on risk scores predicting mortality in TC is sparse. We developed a machine-learning...

Determining health care cost drivers in older Hodgkin lymphoma survivors using interpretable machine learning methods.

Journal of managed care & specialty pharmacy
BACKGROUND: The cost of health care for patients with Hodgkin lymphoma (HL) is projected to rise, making it essential to understand expenditure drivers across different demographics, including the older adult population. Although older HL patients co...

Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study.

The Lancet. Digital health
BACKGROUND: Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of ...