Autoinflammation of unknown origin remains amongst the most enigmatic of systemic autoinflammatory disorders (SAID), with systemic autoinflammatory symptoms in the absence of a molecular or clinical diagnosis with a recognized SAID. Here, we aim to u... read more
BACKGROUND: As artificial intelligence (AI) becomes increasingly integrated into education, understanding student perceptions of AI-generated support is critical. This pilot study examined how Doctor of Nursing Practice (DNP) students evaluate statis... read more
Recent advancements in artificial intelligence have led to increased interest in predictive modeling across various domains, including medicine. Although numerous metrics have been established for binary classification, the growing adoption of multi-... read more
Artificial intelligence (AI) is increasingly applied in healthcare, but concerns remain about bias affecting under-represented groups. We investigated whether skin tone is systematically encoded in hyperspectral imaging data and how this affects clas... read more
Clinical and experimental dental research
Apr 1, 2026
OBJECTIVES: This systematic review and meta-analysis aimed to synthesize the available evidence on the use of AI in dental diagnostic decision-making and treatment planning, evaluating both diagnostic accuracy and its influence on clinical decision-m... read more
Clinical and experimental dental research
Apr 1, 2026
OBJECTIVES: Accurate assessment of dental implant stability is critical for predicting osseointegration outcomes and guiding clinical decision-making. Resonance frequency analysis (RFA) is a widely adopted non-invasive method for measuring implant st... read more
Journal of applied clinical medical physics
Apr 1, 2026
PURPOSE: Ambiguous or incomplete documentation is a recurrent bottleneck in radiation oncology workflows, leading to inefficiencies in communication and potential treatment delays. Large language models (LLMs) pose a solution to addressing these ambi... read more
PURPOSE: To develop a deep learning (DL)-based automated segmentation model for rectal cancer on T2-weighted (T2W) magnetic resonance (MR) images. MATERIALS AND METHODS: A total of 458 patients who underwent baseline rectal MR imaging were retrospect... read more
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