PURPOSE: Accurate detection of proximal dental caries on panoramic radiographs is essential for effective treatment planning and preventive care. While senior dental students gradually develop interpretative competence during their training, artifici...
BACKGROUND: Sepsis poses a significant threat in emergency settings, necessitating tools for early and interpretable risk assessment. This study aimed to develop a robust explainable boosting machine (EBM) model, one of the explainable artificial int...
Early diagnosis and personalized intervention for Autism Spectrum Disorder (ASD) in children can potentially improve developmental outcomes, though current methods often lack scalability and adaptability. This study introduces an integrated system co...
Variable physiological [F]FDG uptake patterns and a lack of labelled data make it challenging to automatically distinguish normal from pathological suspicious uptake in whole-body PET/CT imaging. We propose a deep learning method that generates patie...
This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimensional tabular data from 9,134 patients across four hospitals. Seven CML m...
Text-to-image (T2I) artificial intelligence models are being increasingly explored in medical education, yet their utility in ophthalmology remains unclear. Slit-lamp anterior segment photography, as a cornerstone of ophthalmic training, provides an ...
This study investigates the developmental status and influencing factors of artificial intelligence (AI) literacy and computational thinking (CT) literacy among undergraduates in China's "four new" majors. Guided by the Technology Acceptance Model, S...
Chimeric antigen receptor (CAR) T-cell therapy has shown significant success in treating diffuse large B-cell lymphoma (DLBCL). The initial step involves collecting autologous CD3 lymphocytes through apheresis, in which obtaining an adequate CD3 cell...
This study introduces a non‑invasive approach for neurovisual classification of geometric shapes by capturing and decoding laser‑speckle patterns reflected from the human striate cortex. Using a fast digital camera and deep neural networks (DNN), we ...
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