Latest AI and machine learning research in surveillance for healthcare professionals.
BACKGROUND: Recently, deep learning (DL)-based noise reduction (DLNR) has been introduced in clinically used digital radiography (DR) systems, reporting superior performance over conventional algorithms. However, DLNR algorithms often operate as "black boxes" with nonlinear behavior, making it essential to understand the impact of such processing on image quality under different imaging conditions...
Artificial intelligence (AI) systems are increasingly integrated into clinical practice, where they demonstrate potential to mitigate adverse events through enhanced patient monitoring and decision support. However, these AI systems also introduce ethical concerns around care standards and surveillance. Literature on ethically acceptable healthcare AI remains broadly theoretical, limiting its prac...
This paper focuses on school climate indicators, which have been previously linked with aspects of students' well-being and school-related success, to...
PURPOSE: To evaluate adoption patterns and attitudes toward generative AI-particularly large language models (LLMs) such as ChatGPT-among medical phys...
Clinical research published in internal medicine journals relies heavily on statistical analysis and quantitative inference, making the quality of sta...
Artificial intelligence (AI) has emerged as a promising tool to detect early dysplasia in Barrett's esophagus (BE). However, the cost-effectiveness of...
BACKGROUND: Evidence-based decision-making in healthcare relies heavily on routine health information. However, in many low-income and middle-income c...
BACKGROUND AND OBJECTIVE: Manual data extraction is a major bottleneck in uro-oncology, limiting research and quality assurance. Although artificial i...
Bladder cancer carries one of the highest lifetime costs among malignancies, and accurate distinction between non-muscle-invasive and muscle-invasive ...
PURPOSE: To develop the REporting checklist for FoundatIon and large laNguagE models (REFINE), an international reporting guideline for transparent an...
BACKGROUND: Artificial intelligence (AI)-enabled wearable devices are rapidly emerging in rehabilitation and motor function assessment for patients wi...
Background Gastric intestinal metaplasia (GIM) is a well-established precancerous lesion and key biomarker for assessing the risk of gastric cancer. H...
Pancreatic neuroendocrine tumors (PanNETs) are increasingly diagnosed, reflecting greater clinical awareness, improved imaging, and revised classifica...
The exponential growth of video data from surveillance and online platforms has heightened the demand for intelligent, explainable systems capable of ...
BACKGROUND: Developments in artificial neural networks (ANNs) offer significant promise for cancer screening and risk prediction, with the potential t...
OBJECTIVE: Insidiousness is a hallmark of metachronous liver metastasis. Owing to the absence of a comprehensive machine-learning model integrating sy...
Data mining is the systematic process of extracting useful knowledge from large multimodal datasets and is increasingly enabled by artificial intellig...
Respiratory diseases are increasingly influenced by meteorological variability, yet few studies have applied high-resolution environmental data and ad...
Large language models (LLMs) show potential in clinical reporting, yet current multimodal systems remain unreliable for interpreting panoramic radiogr...
Timely access to reliable public health data is a critical determinant of effective response to health emergencies, including disease outbreaks, clima...