AIMC Topic: Cross-Sectional Studies
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Performance of Artificial Intelligence Chatbots on Ultrasound Examinations: Cross-Sectional Comparative Analysis.
JMIR medical informatics
Jan 9, 2025
BACKGROUND: Artificial intelligence chatbots are being increasingly used for medical inquiries, particularly in the field of ultrasound medicine. However, their performance varies and is influenced by factors such as language, question type, and topi...
The importance of clinical experience in AI-assisted corneal diagnosis: verification using intentional AI misleading.
Scientific reports
Jan 9, 2025
We developed an AI system capable of automatically classifying anterior eye images as either normal or indicative of corneal diseases. This study aims to investigate the influence of AI's misleading guidance on ophthalmologists' responses. This cross...
Widespread use of ChatGPT and other Artificial Intelligence tools among medical students in Uganda: A cross-sectional study.
PloS one
Jan 9, 2025
BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. Consequently, i...
Liver fibrosis stage classification in stacked microvascular images based on deep learning.
BMC medical imaging
Jan 7, 2025
BACKGROUND: Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fib...
Machine learning algorithms to predict depression in older adults in China: a cross-sectional study.
Frontiers in public health
Jan 7, 2025
OBJECTIVE: The 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among China's older adult population and to determine the noteworthy aspects resulting in depression.
The relationships of personality traits on perceptions and attitudes of dentistry students towards AI.
BMC medical education
Jan 6, 2025
INTRODUCTION: Artificial intelligence (AI) has gained significant attention in dentistry due to its potential to revolutionize practice and improve patient outcomes. However, dentists' views and attitudes toward technology can affect the application ...
Assessing fecal contamination from human and environmental sources using as an indicator in rural eastern Ethiopian households-a cross-sectional study from the EXCAM project.
Frontiers in public health
Jan 6, 2025
INTRODUCTION: Enteric pathogens are a leading causes of diarrheal deaths in low-and middle-income countries. The Exposure Assessment of Infections in Rural Ethiopia (EXCAM) project, aims to identify potential sources of bacteria in the genus and, m...
Examining sustainable hospitality practices and employee turnover in Pakistan: The interplay of robotics awareness, mutual trust, and technical skills development in the age of artificial intelligence.
Journal of environmental management
Jan 5, 2025
Integrating robots and artificial intelligence (AI) into workplaces is becoming increasingly prevalent across various sectors, including hospitality. This trend has raised concerns regarding employee anxiety and the potential for higher turnover inte...
Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study.
Scientific reports
Jan 4, 2025
Developing a new diagnostic prediction model for osteoarthritis (OA) to assess the likelihood of individuals developing OA is crucial for the timely identification of potential populations of OA. This allows for further diagnosis and intervention, wh...
Predicting Gestational Diabetes Mellitus in the first trimester using machine learning algorithms: a cross-sectional study at a hospital fertility health center in Iran.
BMC medical informatics and decision making
Jan 3, 2025
BACKGROUND: Gestational Diabetes Mellitus (GDM) is a common complication during pregnancy. Late diagnosis can have significant implications for both the mother and the fetus. This research aims to create an early prediction model for GDM in the first...
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