AIMC Topic: Cross-Sectional Studies

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Identifying melanoma among benign simulators - Is there a role for deep learning convolutional neural networks? (MelSim Study).

European journal of cancer (Oxford, England : 1990)
IMPORTANCE: Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learnin...

Attitudes, Perceptions, and Factors Influencing the Adoption of AI in Health Care Among Medical Staff: Nationwide Cross-Sectional Survey Study.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) has demonstrated transformative potential in the health care field; yet, its clinical adoption faces challenges such as inaccuracy, bias, and data privacy concerns. As the primary operators of AI systems, phys...

Liver MRI proton density fat fraction inference from contrast enhanced CT images using deep learning: A proof-of-concept study.

PloS one
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease worldwide, affecting over 30% of the global general population. Its progressive nature and association with other chronic diseases make...

Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households.

BMC health services research
BACKGROUND: Despite the National Health Insurance (NHI) system implemented in South Korea, concerns persist regarding access to health coverage for low-income households. To address this issue, this study aims to use machine learning-based data minin...

Classifying Patient Complaints Using Artificial Intelligence-Powered Large Language Models: Cross-Sectional Study.

Journal of medical Internet research
BACKGROUND: Patient complaints provide valuable insights into the performance of health care systems, highlighting potential risks not apparent to staff. Patient complaints can drive systemic changes that enhance patient safety. However, manual categ...

Attitudes and readiness to adopt artificial intelligence among healthcare practitioners in Pakistan's resource-limited settings.

BMC health services research
BACKGROUND: Artificial Intelligence (AI) can empower clinicians to make data-driven decisions, treatments and streamline administrative tasks. However, it is vital to understand their perception towards AI for seamless implementation in practice. The...

An AI-driven video based goniometer for knee joint range of motion (ROM) Assessment: Reliability and validity compared to traditional goniometry.

Computers in biology and medicine
BACKGROUND: Accurate measurement of knee joint range of motion (ROM) is crucial in clinical and rehabilitation settings. Traditional goniometry, which is widely used, requires calibration and is subject to human errors and measurement inconsistencies...

Automated classification of skeletal malocclusion in German orthodontic patients.

Clinical oral investigations
OBJECTIVES: Precisely diagnosing skeletal class is mandatory for correct orthodontic treatment. Artificial intelligence (AI) could increase efficiency during diagnostics and contribute to automated workflows. So far, no AI-driven process can differen...

Prevalence, associated risk factors and satellite imagery analysis in predicting soil-transmitted helminth infection in Nakhon Si Thammarat Province, Thailand.

Scientific reports
Soil-transmitted helminth (STH) infections remain a significant public health concern in rural areas, often leading to nutritional and physical impairment, particularly in children. This study aimed to assess the prevalence and associated factors of ...