AIMC Topic: Cross-Sectional Studies

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Differentiating adolescent suicidal and nonsuicidal self-harm with artificial intelligence: Beyond suicidal intent and capability for suicide.

Journal of affective disorders
Clinical differentiation between adolescent suicidal self-harm (SSH) and nonsuicidal self-harm (NSSH) is a significant challenge for mental health professionals, and its feasibility is controversial. The aim of the present study was to determine whet...

The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes.

Respiratory research
BACKGROUND: Tuberculous pleural effusion (TPE) is a challenging extrapulmonary manifestation of tuberculosis, with traditional diagnostic methods often involving invasive surgery and being time-consuming. While various machine learning and statistica...

Future pharmacy practitioners' insights towards integration of artificial intelligence in healthcare education: Preliminary findings from Karachi, Pakistan.

PloS one
UNLABELLED: In an evolutionary era of medical education, "Artificial intelligence" (AI) is applied to replicate human intellect, encompassing abilities, logical reasoning and effective problem-solving skills. Previous research has explored the attitu...

Generative Artificial Intelligence in Medical Education-Policies and Training at US Osteopathic Medical Schools: Descriptive Cross-Sectional Survey.

JMIR medical education
BACKGROUND: Interest has recently increased in generative artificial intelligence (GenAI), a subset of artificial intelligence that can create new content. Although the publicly available GenAI tools are not specifically trained in the medical domain...

Generative artificial intelligence (GAI) usage guidelines for scholarly publishing: a cross-sectional study of medical journals.

BMC medicine
BACKGROUND: Generative artificial intelligence (GAI) has developed rapidly and been increasingly used in scholarly publishing, so it is urgent to examine guidelines for its usage. This cross-sectional study aims to examine the coverage and type of re...

Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study.

Journal of cardiothoracic surgery
BACKGROUND: Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + ...

Exploring the importance of clinical and sociodemographic factors on self-rated health in midlife: A cross-sectional study using machine learning.

International journal of medical informatics
BACKGROUND: Self-rated health (SRH) is influenced by various factors, including clinical and sociodemographic characteristics. However, in the context of Brazil, we still lack a clear understanding of the relative importance of these factors and how ...

Understanding Providers' Attitude Toward AI in India's Informal Health Care Sector: Survey Study.

JMIR formative research
BACKGROUND: Tuberculosis (TB) is a major global health concern, causing 1.5 million deaths in 2020. Diagnostic tests for TB are often inaccurate, expensive, and inaccessible, making chest x-rays augmented with artificial intelligence (AI) a promising...

Discovering Vitamin-D-Deficiency-Associated Factors in Korean Adults Using KNHANES Data Based on an Integrated Analysis of Machine Learning and Statistical Techniques.

Nutrients
: Vitamin D deficiency (VDD) is a global health concern associated with metabolic disease and immune dysfunction. Despite known risk factors like limited sun exposure, diet, and lifestyle, few studies have explored these factors comprehensively on a ...

Advancing Alzheimer's disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study.

BMJ open
OBJECTIVES: Alzheimer's disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited access...