AIMC Topic: Cross-Sectional Studies
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Prediction of COVID-19 patients' participation in financing informal care using machine learning methods: willingness to pay and willingness to accept approaches.
BMC health services research
Jul 10, 2024
BACKGROUND: Informal care plays an essential role in managing the COVID-19 pandemic. Expanding health insurance packages that reimburse caregivers' services through cost-sharing policies could increase financial resources. Predicting payers' willingn...
Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis.
Disability and rehabilitation
Jul 8, 2024
PURPOSE: Wearable robotic devices are currently being developed to improve upper limb function for individuals with hemiparesis after stroke. Incorporating the views of clinicians during the development of new technologies can help ensure that end pr...
Awareness and Perceptions of ChatGPT Among Academics and Research Professionals in Riyadh, Saudi Arabia: Implications for Responsible AI Use.
Medical science monitor : international medical journal of experimental and clinical research
Jul 8, 2024
BACKGROUND Chat Generative Pre-Trained (ChatGPT) Transformer was created by OpenAI and has a powerful tool used in research. This study aimed to assess the awareness and perceptions of ChatGPT among researchers and academicians at King Saud Universit...
Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence.
BMJ open ophthalmology
Jul 5, 2024
OBJECTIVES: This study aimed to quantitatively evaluate optic nerve head and retinal vascular parameters in children with hyperopia in relation to age and spherical equivalent refraction (SER) using artificial intelligence (AI)-based analysis of colo...
Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features.
European journal of applied physiology
Jul 3, 2024
PURPOSE: Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical ...
Prediction of chromosomal abnormalities in the screening of the first trimester of pregnancy using machine learning methods: a study protocol.
Reproductive health
Jul 3, 2024
BACKGROUND: For women in the first trimester, amniocentesis or chorionic villus sampling is recommended for screening. Machine learning has shown increased accuracy over time and finds numerous applications in enhancing decision-making, patient care,...
Measuring Vertical Jump Height With Artificial Intelligence Through a Cell Phone: A Validity and Reliability Report.
Journal of strength and conditioning research
Jul 2, 2024
Erik, HT, Onn, SW, and Montalvo, S. Vertical jump height with artificial intelligence through a cell phone: a validity and reliability report. J Strength Cond Res 38(9): e529-e533, 2024-This study estimated the reliability and validity of an artifici...
Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis.
JMIR dermatology
Jul 2, 2024
BACKGROUND: Dermatology is an ideal specialty for artificial intelligence (AI)-driven image recognition to improve diagnostic accuracy and patient care. Lack of dermatologists in many parts of the world and the high frequency of cutaneous disorders a...
Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study.
Frontiers in public health
Jul 2, 2024
OBJECTIVES: The application of artificial intelligence (AI) in healthcare is an important public health issue. However, few studies have investigated the perceptions and attitudes of healthcare professionals toward its applications in nursing. This s...
Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia.
PloS one
Jul 2, 2024
IMPORTANCE: Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates.
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