Latest AI and machine learning research in surveys for healthcare professionals.
BACKGROUND: Artificial intelligence (AI) prediction models can accurately identify high-risk populations by integrating multi-dimensional clinical data, providing decision support for doctors in formulating individualized discharge plans and optimizing follow-up intervention strategies, thereby reducing the risk of readmission from the source. Currently, the number of AI prediction models for read...
INTRODUCTION: The widespread use of smartphones presents a remarkable opportunity for real-time management of orthodontic treatment. The development of orthodontic smartphone applications, such as Dental Monitoring (DM), epitomises the use of Artificial Intelligence Driven Remote Monitoring (AIDRM) for sophisticated image analysis. The primary aim of this study is to evaluate the validity of DM's ...
Background: Health technology assessment (HTA) increasingly informs reimbursement, adoption, scale-up, and disinvestment decisions, yet many evidentia...
Assessing students' learning outcomes and abilities has always been a key link for English translation education in universities. However, traditional...
Deep learning has significantly advanced medical imaging analysis (MIA), achieving state-of-the-art performance across diverse clinical tasks. However...
Early identification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), is important for timely clinical assessment...
BACKGROUND: While machine learning (ML) models demonstrate high predictive accuracy, recent studies reveal that ML models underperform for smaller sub...
Quantum convolutional neural networks (QCNNs) are a highly appealing architecture that combines quantum computing and deep learning. Inspired by class...
BACKGROUND: Effective postdischarge management is essential for maintaining disease control and improving long-term outcomes in rheumatoid arthritis (...
OBJECTIVES: Healthcare systems are now funding implementation of artificial intelligence (AI) algorithms in radiology, which will change the experienc...
BACKGROUND: People with stroke face a high mortality risk, and an accurate prediction model is essential to the guidance of clinical decision-making i...
OBJECTIVES: To identify the lowest sensitivity and specificity that physicians and the general population consider acceptable for medical artificial i...
The number of people in Germany requiring care has risen steadily, increasing the importance of informal care. This form of care is often associated w...
OBJECTIVE: To explore the role of reinforcement learning (RL) in vision-language models (VLMs) for cardiovascular disease (CVD) decision support and a...
BACKGROUND: In vitro testing is a fundamental approach for advancing spinal biomechanics research. However, existing loading methods still exhibit not...
BACKGROUND: In clinical practice, accurately estimating a patient's prognosis is essential for clinical decision making. Prognostic prediction models ...
Tumor dynamic models are vital for evaluating oncology treatments and guiding clinical drug development decisions. However, few studies rigorously ass...
BACKGROUND: Active trachoma remains above elimination thresholds in Ethiopia, yet tools for fine-scale targeting are limited. We developed and interna...
BACKGROUND: Collection of multimodal data (video, audio, and text) can yield digital biomarkers relevant to mental health, fatigue, and cognition. How...
BACKGROUND: In the field of patient monitoring, there often remains a gap between clinical needs and the monitoring technologies available from indust...