AI Medical Compendium Topic

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Cross-Sectional Studies

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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...

Unveiling GPT-4V's hidden challenges behind high accuracy on USMLE questions: Observational Study.

Journal of medical Internet research
BACKGROUND: Recent advancements in artificial intelligence, such as GPT-3.5 Turbo (OpenAI) and GPT-4, have demonstrated significant potential by achieving good scores on text-only United States Medical Licensing Examination (USMLE) exams and effectiv...

Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI).

BMJ open
INTRODUCTION: Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approa...

Predictors of depression among Chinese college students: a machine learning approach.

BMC public health
BACKGROUND: Depression is highly prevalent among college students, posing a significant public health challenge. Identifying key predictors of depression is essential for developing effective interventions. This study aimed to analyze potential depre...

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.

JMIR medical informatics
BACKGROUND: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute to rising rates of obesity and chronic diseases like type 2 diabetes and heart disease. Particularly personalized interventions have been shown to be e...

Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists' Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study.

JMIR formative research
BACKGROUND: The COVID-19 pandemic has significantly strained health care systems globally, leading to an overwhelming influx of patients and exacerbating resource limitations. Concurrently, an "infodemic" of misinformation, particularly prevalent in ...

Development and usability evaluation of a nurse-led clinical decision support system (AI-AntiDelirium) for management of intensive care unit delirium: A mixed methods study.

Applied nursing research : ANR
BACKGROUND: Clinical decision support systems (CDSS) have been identified to aid clinical decision-making, but few studies focus on the application of CDSS in intensive care unit (ICU) delirium, and particularly usability testing is not employed. We ...

Quality of Information Provided by Artificial Intelligence Chatbots Surrounding the Management of Vestibular Schwannomas: A Comparative Analysis Between ChatGPT-4 and Claude 2.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
OBJECTIVE: To examine the quality of information provided by artificial intelligence platforms ChatGPT-4 and Claude 2 surrounding the management of vestibular schwannomas.

Deep learning opportunistic screening for osteoporosis and osteopenia using radiographs of the foot or ankle - A pilot study.

European journal of radiology
BACKGROUND: The gold standard method for diagnosing low bone mineral density (BMD) is using dual-energy X-ray absorptiometry (DXA) however, most patients with low BMD are often not screened. We aimed to create a deep learning (DL) model to screen for...