Latest AI and machine learning research in alternative medicine for healthcare professionals.
Accurate photometric redshift estimation is critical for observational cosmology, especially in large-scale surveys where spectroscopic measurements are impractical. Traditional approaches include template fitting and machine learning, each with distinct strengths and limitations. We present a hybrid method that integrates template fitting with deep learning using physics-guided neural networks....
Grounding large language models (LLMs) in verifiable external sources is a well-established strategy for generating reliable answers. Retrieval-augmented generation (RAG) is one such approach, particularly effective for tasks like question answering: it retrieves passages that are semantically related to the question and then conditions the model on this evidence. However, multi-hop questions, s...
BACKGROUND: Artificial Intelligence (AI) models hold promise as useful tools in healthcare practice. We aimed to develop and assess AI models for auto...
Surface defect detection plays a critical role in industrial quality inspection. Recent advances in artificial intelligence have significantly enhan...
Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) presents a significant challenge, with recurrence rates ranging from 8% to ...
Existing 3D visual grounding methods rely on precise text prompts to locate objects within 3D scenes. Speech, as a natural and intuitive modality, o...
In this paper, we construct two research objectives: i) explore the learned embedding space of BiomedCLIP, an open-source large vision language mode...
To address the identification challenges caused by morphological similarities in Root and Rhizome Chinese Herbal (RRCH), this study developed a discri...
Background: Deep learning has significantly advanced ECG arrhythmia classification, enabling high accuracy in detecting various cardiac conditions. ...
Automated question answering (QA) over electronic health records (EHRs) can bridge critical information gaps for clinicians and patients, yet it dem...
Deep learning has previously shown success in automatically generating morphological traits that carry a phylogenetic signal. In this paper, we explor...
For the immanent challenge of insufficiently annotated samples in the medical field, semi-supervised medical image segmentation (SSMIS) offers a pro...
Brain tumors, regardless of being benign or malignant, pose considerable health risks, with malignant tumors being more perilous due to their swift ...
The expanding domain of digital mental health is transitioning beyond traditional telehealth to incorporate smartphone apps, virtual reality, and gene...
Integrating Traditional Chinese Medicine (TCM) and Modern Medicine faces significant barriers, including the absence of unified frameworks and standar...
Cancer pain management (CPM) is crucial in oncology care, with current approaches including pharmacotherapy, radiotherapy, chemotherapy, nerve blocks,...
We suggest AI framework for UC diagnosis using Sparse Autoencoders (SA) for feature extraction combined with Explainable AI (XAI) utilizing Grad-CAM t...
Tissue segmentation in histopathological images plays a crucial role in computational pathology, owing to its significant potential to indicate the pr...
BACKGROUND AND OBJECTIVES: AI has emerged as a transformative force in clinical medicine, changing the diagnosis, treatment, and management of patient...
Characterizing age-related alterations in brain networks is crucial for understanding aging trajectories and identifying deviations indicative of neur...