AIMC Topic: Cross-Sectional Studies
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Assessing GPT-4's accuracy in answering clinical pharmacological questions on pain therapy.
British journal of clinical pharmacology
Mar 11, 2025
AIMS: This study aimed to evaluate the accuracy and completeness of GPT-4, a large language model, in answering clinical pharmacological questions related to pain therapy, with a focus on its potential as a tool for delivering patient-facing medical ...
A sentiment analysis of YouTube videos from donor-conceived people, utilizing artificial intelligence (ChatGPT).
Reproductive biomedicine online
Mar 10, 2025
RESEARCH QUESTION: What is the tone of donor-conceived people (DCP) towards their donor-conceived status in YouTube videos assessed for sentiment analysis via ChatGPT, a natural language-processing tool?
Awareness of the Role of Artificial Intelligence in Health Care among Undergraduate Nursing Students: A Descriptive Cross-Ssectional Study.
Nurse education today
Mar 8, 2025
BACKGROUND: Artificial intelligence (AI) has the potential to revolutionize healthcare by improving efficiency and reducing errors; however, challenges such as inadequate funding and lack of awareness among healthcare professionals hinder its integra...
Role of oxidative balance score in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: Insights from traditional and machine learning approaches.
Redox biology
Mar 7, 2025
OBJECTIVES: To evaluate the roles of oxidative balance score (OBS) in staging and mortality risk of cardiovascular-kidney-metabolic syndrome (CKM).
Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea.
BMC medical informatics and decision making
Mar 7, 2025
BACKGROUND: Retinal vein occlusion (RVO) is a leading cause of vision loss globally. Routine health check-up data-including demographic information, medical history, and laboratory test results-are commonly utilized in clinical settings for disease r...
A machine learning approach to predict treatment efficacy and adverse effects in major depression using CYP2C19 and clinical-environmental predictors.
Psychiatric genetics
Mar 5, 2025
BACKGROUND: Major depressive disorder (MDD) is among the leading causes of disability worldwide and treatment efficacy is variable across patients. Polymorphisms in cytochrome P450 2C19 (CYP2C19) play a role in response and side effects to medication...
Use of natural language processing method to identify regional anesthesia from clinical notes.
Regional anesthesia and pain medicine
Mar 5, 2025
INTRODUCTION: Accurate data capture is integral for research and quality improvement efforts. Unfortunately, limited guidance for defining and documenting regional anesthesia has resulted in wide variation in documentation practices, even within indi...
Automated structured data extraction from intraoperative echocardiography reports using large language models.
British journal of anaesthesia
Mar 3, 2025
BACKGROUND: Consensus-based large language model (LLM) ensembles might provide an automated solution for extracting structured data from unstructured text in echocardiography reports.
Performance of a Deep Learning Diabetic Retinopathy Algorithm in India.
JAMA network open
Mar 3, 2025
IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and diabetic macular edema (DME), to date, little published data exist on the clinical performance of thes...
Interpretation of cardiopulmonary exercise test by GPT - promising tool as a first step to identify normal results.
Expert review of respiratory medicine
Mar 2, 2025
BACKGROUND: Cardiopulmonary exercise testing (CPET) is used in the evaluation of unexplained dyspnea. However, its interpretation requires expertise that is often not available. We aim to evaluate the utility of ChatGPT (GPT) in interpreting CPET res...
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