Latest AI and machine learning research in surveys for healthcare professionals.
BACKGROUND: Artificial intelligence (AI) models are being increasingly integrated into clinical care. Moreover, the availability of publicly accessible AI resources makes them attractive to patients seeking clinical information. Little is known regarding the use of large language models as patient resources for navigating major cancer diagnoses. OBJECTIVE: This study aimed to evaluate the content,...
OBJECTIVE: To develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning methods to improve the efficiency, fairness, and accuracy of healthcare feedback analysis. MATERIALS AND METHODS: We designed a multi-component pipeline incorporating sentiment analysis, key theme extraction, clinical Named Entity Recognition (NER), a...
Traditional anthropometric methods for personalising equipment in high-stakes professions are often costly, time-consuming, and lack scalability. This...
BACKGROUND: Accurate evaluation of skin lesions is an essential component of dermatological examination, yet it can be time-consuming and subject to i...
The rapid integration of artificial intelligence (AI) into higher education is producing divergent learning behaviors, as student AI anxiety appears t...
BACKGROUND: Disability assessment in dementia is important for care planning, but the full World Health Organization Disability Assessment Schedule 2....
BACKGROUND: Generative artificial intelligence (GenAI) tools are increasingly used in scientific research to support literature searches, evidence syn...
OBJECTIVES: Artificial intelligence (AI)-assisted endoscopy has been developed for the early detection of upper gastrointestinal cancer; however, its ...
The purpose of this study is to validate a deep learning-based vision transformer for automated quantification and segmentation of abdominal adipose t...
Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enab...
Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in real-world visual applications. To address thi...
As machine learning evolves, domain generalization (DG) and domain adaptation (DA) have become crucial for improving model robustness across diverse e...
BACKGROUND: Pain is a multifaceted and subjective phenomenon frequently experienced by patients in intensive care units. In non-communicating populati...
Crowdsourcing is now widely used in disaster management. However, restricted access to social media of vulnerable people in poor areas will pose a ris...
BACKGROUND: The growing number of hypoglycaemia risk prediction models for Type 2 diabetes mellitus (T2DM) underscores the need for systematic evaluat...
BACKGROUND AND OBJECTIVE: Multi-class brain tumor classification from magnetic resonance imaging must achieve high diagnostic accuracy while maintaini...
OBJECTIVE: To systematically evaluate the methodological quality and diagnostic performance of artificial intelligence (AI) applications, specifically...
This critical review synthesizes the ethical, technical, and equity dilemmas emerging from the rapid clinical translation of liquid biopsy technologie...
BACKGROUND: Artificial intelligence and machine learning (AI/ML) may strengthen hospital infection prevention and control (IPC) through automated surv...
BACKGROUND: This study aimed to evaluate and compare the performance of five publicly accessible large language models (LLMs)-based chatbots, ChatGPT-...