AIMC Topic: Cross-Sectional Studies

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Comparing AI-Assisted Problem-Solving Ability With Internet Search Engine and e-Books in Medical Students With Variable Prior Subject Knowledge: Cross-Sectional Study.

JMIR medical education
BACKGROUND: Artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT (OpenAI), is rapidly influencing medical education. Its effectiveness for students with varying levels of prior knowledge remains underexplored.

Machine learning for screening laryngopharyngeal reflux symptoms in college students: a cross-sectional study.

Annals of medicine
BCKGROUND: Laryngopharyngeal reflux (LPR) is a widespread global health issue. Its recurring symptoms and impact on quality of life create significant economic burdens for individuals and society. To examine the links between lifestyle, diet, and LPR...

How perceived stress and social support shape non-communicable disease risks beyond traditional factors: a machine learning perspective.

BMC public health
BACKGROUND: Psychosocial factors such as perceived stress and social support have been increasingly recognized as significant contributors to non-communicable diseases (NCDs). However, their predictive value in comparison to traditional risk factors ...

The impact of positive attitude towards artificial intelligence and multiple mediation functions of satisfying basic psychological needs.

Scientific reports
With the wide application of Artificial Intelligence (AI) in the health field, this study aims to explore the intrinsic connections among AI, individual psychology, and digital health, and reveal the multiple mediating roles of positive attitudes tow...

Generation of a Free-Living Ground-Truth Validation Dataset for Wearable Measures of Physical Activity, Sedentary Behavior, Sleep, and Heart Rate in Adults (OxWEARS): Protocol for a Cross-Sectional Study.

JMIR research protocols
BACKGROUND: Wearable devices enable continuous measurement of physical activity, sedentary behavior, sleep, and heart rate under free-living conditions. However, most validation studies rely on small, homogeneous samples; are conducted under laborato...

Lymphocytes and related inflammatory factors as predictors of metabolic syndrome risk in shift workers: A machine learning approach based on large-scale population data.

PloS one
BACKGROUND: Metabolic syndrome (MetS) is characterized by chronic inflammation and can be worsened by circadian disruption, which is common among shift work. Machine learning can predict the risk of MetS in shift workers using inflammatory biomarkers...

Health-related quality of life among healthcare workers: a comparative analysis using regression, conditional tree and forests.

BMC public health
BACKGROUND: Considering the potential importance of health care workers (HCWs) in maintaining and improving the health of society, we decided to investigate the factors affecting the health-related quality of life (HRQoL) of HCWs using machine learni...

Using AI-Based Virtual Simulated Patients for Training in Psychopathological Interviewing: Cross-Sectional Observational Study.

JMIR medical education
BACKGROUND: Virtual simulated patients (VSPs) powered by generative artificial intelligence (GAI) offer a promising tool for training clinical interviewing skills; yet, little is known about how different system- and user-level variables shape studen...

Impact of AI Literacy on Well-Being Among Nursing Students-Mediating Roles of Empowerment and Anxiety: Cross-Sectional Study.

JMIR nursing
BACKGROUND: The integration of artificial intelligence (AI) in health care is changing nursing practice, and it calls for the acquisition of AI literacy by students, which includes knowledge, skills, and attitudes. An understanding of the effect of A...