The authors introduce a novel framework that integrates federated learning with Graph Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop methodologies. This advanced framework innovatively employs collaborative voting mechan...
Artificial Intelligence (AI) and Machine Learning (ML) models are increasingly deployed on biomedical and health data to shed insights on biological mechanism, predict disease outcomes, and support clinical decision-making. However, ensuring model va...
PURPOSE: The increasing use of AI tools, particularly large language models like ChatGPT, in academic research has raised significant questions about authorship and transparency. This commentary emphasizes the need for a standardized AI contributions...
PURPOSE: Retrospectively compare image quality, radiologist diagnostic confidence, and time for images to reach PACS for contrast enhanced abdominopelvic CT examinations created on the scanner console by technologists versus those generated automatic...
BACKGROUND AND AIMS: Cecal intubation in colonoscopy relies on self-reporting. We developed an artificial intelligence-based cecum recognition system (AI-CRS) for post-hoc verification of cecal intubation and explored its impact on adenoma metrics.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Sep 18, 2024
BACKGROUND: Artificial intelligence (AI) algorithms for the detection of retinoblastoma (RB) by fundus image analysis have been proposed as a potentially effective technique to facilitate diagnosis and screening programs. However, doubts remain about...
Three types of highly promising small RNA therapeutics, namely, small interfering RNAs (siRNAs), microRNAs (miRNAs) and the RNA subtype of antisense oligonucleotides (ASOs), offer advantages over small-molecule drugs. These small RNAs can target any ...
AJR. American journal of roentgenology
Sep 18, 2024
Radiologists' traditional role in the diagnostic process is to respond to specific clinical questions and reduce uncertainty enough to permit treatment decisions to be made. This charge is rapidly evolving due to forces such as artificial intelligenc...
Canonical cases of learning involve novel observations external to the mind, but learning can also occur through mental processes such as explaining to oneself, mental simulation, analogical comparison, and reasoning. Recent advances in artificial in...
AJR. American journal of roentgenology
Sep 18, 2024
Generative artificial intelligence (AI) and large language models (LLMs) are increasingly being recognized as tools with the potential to transform many industries, including health care. Implementation and use of these tools among radiologists is li...
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