Latest AI and machine learning research in surveys for healthcare professionals.
The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning. These large-scale artificial intelligence systems, trained on extensive multimodal and multi-center datasets, demonstrate remarkable versatility across diverse medical applications. However, their integration into clinical...
Gestational diabetes mellitus (GDM) significantly increases the risk of developing type 2 diabetes (T2D) postpartum. Early identification of high-risk women using machine learning (ML) models could enable targeted interventions and improve outcomes. This systematic review aims to evaluate the performance, predictive features, and methodological quality of ML models designed to predict the transiti...
The investigation and diagnosis of hematologic malignancy using blood cell image analysis are major and emerging subjects that lie at the intersection...
Automatic pain assessment for non-communicative children is in high demand. However, the availability of related training datasets remains limited. Th...
Advanced Metering Infrastructure (AMI), as a critical data collection and communication hub within the smart grid architecture, is highly vulnerable t...
Postoperative pain, anxiety, and psychological distress significantly impact surgical recovery, yet conventional management strategies often lack pers...
Federated learning (FL) offers innovative solutions for privacy-preserving distributed machine learning (ML). Different from centralized data collecti...
PURPOSE: Artificial intelligence (AI) is more capable and accessible than ever before. But what does this mean for clinical practice? How can speech-l...
TMDs are a common group of conditions affecting the temporomandibular joint (TMJ) often resulting from factors like injury, stress or teeth grinding. ...
Electronic Health Records (EHRs) store vast amounts of clinical information that are difficult for healthcare providers to summarize and synthesize re...
Foundation models (FMs) are large-scale deep learning models trained on massive datasets, often using self-supervised learning techniques. These model...
In recent years, researchers have explored an innovative approach that leverages real vehicle trajectory data to simultaneously derive traffic state a...
Managing rheumatic diseases requires teamwork, but referral patterns and challenges remain poorly understood. This study explored rheumatologists' per...
There are safety risks when drivers take over the control of autonomous driving vehicles, and reducing unnecessary takeovers is essential to improve d...
Healthcare systems are increasingly integrating artificial intelligence and machine learning (AI/ML) tools into patient care, potentially influencing ...
Mobile Edge Computing (MEC) systems face critical challenges in optimizing computation offloading decisions while maintaining quality of experience (Q...
BACKGROUND The use of artificial intelligence (AI) in dentistry has been increasing, leading to significant changes in diagnosis, treatment planning, ...
The emergence of generative artificial intelligence (GenAI) offers the potential to enhance health economics and outcomes research (HEOR) by streamlin...
BACKGROUND: The purpose of this study is to examine the validity, reliability and methodological quality of delirium scales that have been translated ...
We examine how the seemingly arbitrary way a prompt is posed, which we term "prompt architecture," influences responses provided by large language mod...