Latest AI and machine learning research in alternative medicine for healthcare professionals.
Clinical and population decision-making relies on the systematic evaluation of extensive regulatory evidence. The FDA drug reviews provide detailed information on clinical trial design, enrollment criteria, sample size, randomization, comparators, endpoints, and indications. However, extracting these data is resource-intensive and time-consuming. Generative Artificial Intelligence large language m...
To quantify the amount and certainty of evidence in Cochrane systematic reviews of interventions, and to describe how this evidence has evolved over time. Large-scale meta-research study Cochrane Database of Systematic Reviews (search date April 8, 2025) Cochrane systematic reviews assessing interventions reporting “Summary of findings” tables. Data were automatically extracted using web scraping ...
Traditional, complementary, and integrative medicine (TCIM) describes a broad collection of medical interventions, practices, and belief systems that ...
Magnetocardiography (MCG) captures the magnetic fields generated by myocardial currents, theoretically preserving electrophysiological details lost in...
Lung cancer imaging plays a crucial role in early diagnosis and treatment, where machine learning and deep learning have significantly advanced the ac...
Inborn errors of metabolism (IEMs) are rare genetic conditions with significant morbidity and mortality. Technological advances have increased therape...
AIM: This study aimed to develop a reliable and efficient system for predicting and locating rib fractures in medical images using an ensemble of conv...
Intrinsically disordered proteins and regions are increasingly appreciated for their abundance in the proteome and the many functional roles they pl...
Today, manga has gained worldwide popularity. However, the question of how various elements of manga, such as characters, text, and panel layouts, r...
Strongly lensed quasars provide valuable insights into the rate of cosmic expansion, the distribution of dark matter in foreground deflectors, and t...
As social systems become increasingly complex, legal articles are also growing more intricate, making it progressively harder for humans to identify...
Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, cu...
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Rece...
Semantic location prediction from multimodal social media posts is a critical task with applications in personalized services and human mobility ana...
Infrared-visible object detection (IVOD) seeks to harness the complementary information in infrared and visible images, thereby enhancing the perfor...
Studies of the functional role of the primate ventral visual stream have traditionally focused on object categorization, often ignoring -- despite m...
Brain tumor segmentation models have aided diagnosis in recent years. However, they face MRI complexity and variability challenges, including irregu...
Brain aging involves structural and functional changes and therefore serves as a key biomarker for brain health. Combining structural magnetic reson...
Hyperspectral and Multispectral Image Fusion (HMIF) aims to fuse low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral ima...
Due to their large sizes, volumetric scans and whole-slide pathology images (WSIs) are often processed by extracting embeddings from local regions a...