Latest AI and machine learning research in inflammatory bowel disease for healthcare professionals.
Active feature acquisition (AFA) sequentially selects which features to observe to reach a classification or ranking decision. Its central limitation is reliance on large amount of labeled data to fit probabilistic models guiding acquisition. Large language models (LLMs) supply unsupervised domain knowledge, but are poor sequential planners. Asking one to both know and decide conflates capabilitie...
Active feature acquisition (AFA) sequentially selects which features to observe to reach a classification or ranking decision. Its central limitation is reliance on large amount of labeled data to fit probabilistic models guiding acquisition. Large language models (LLMs) supply unsupervised domain knowledge, but are poor sequential planners. Asking one to both know and decide conflates capabilitie...
Accurate vision-based navigation in monocular endoscopy is difficult due to limited depth cues, weak tissue texture, non-rigid deformation, and substa...
In clinical trials for ulcerative colitis (UC), pathologists assess disease severity through standardized histological indices, including the Geboes S...
Background: Colon capsule endoscopy (CCE) has been proposed as a non-invasive alternative to colonoscopy for colorectal cancer (CRC) screening, offeri...
Medical video diagnosis involves inferring clinical decisions from dynamic tissue responses throughout examination processes. Existing methods rely on...
Microbiome-based machine learning classifiers show increasing promise for disease identification across gastrointestinal, metabolic, and immune-mediat...
Surgical Video Question Answering (VideoQA) provides a promising paradigm for dynamic intraoperative interpretation, enabling real-time decision suppo...
Covalent chemistry has transformed small-molecule drug discovery, yet analogous strategies for proteins remain largely inaccessible because covalent w...
Learning robust representations of polyp tracklets is key to enabling multiple AI-assisted colonoscopy applications, from polyp characterization to au...
Objectives To describe the design, operational safeguards, and early use of ChatIBD, a specialty-specific generative AI platform for inflammatory bowe...
Abstract Objective: Structured extraction from clinical free-text depends on human annotators whose labels are susceptible to errors and knowledge-dri...
Biomedical knowledge graphs have emerged as foundational infrastructure for AI-driven drug discovery, yet their translational impact on novel target i...
Background: Emerging evidence suggests that the oral microbiome may contribute to aberrant gut immune responses in Inflammatory Bowel Disease (IBD). M...
Controllable medical video generation has achieved remarkable progress, but it still lacks interpretability, which requires the alignment of generated...
Background: Current medical large language model (LLM) evaluations largely rely on small collections of cases, whereas rigorous safety testing require...
Early identification and removal of polyps can reduce the risk of developing colorectal cancer. However, the diverse morphologies, complex backgrounds...
Computed tomography (CT) enterography is a primary imaging modality for assessing inflammatory bowel disease (IBD), yet the representational choices t...
The interaction between T cell receptors (TCRs), peptides, and human leukocyte antigens (HLAs) underlies antigen-specific T cell immunity. Despite sub...
Early screening via colonoscopy is critical for colon cancer prevention, yet developing robust AI systems for this domain is hindered by the lack of d...