Latest AI and machine learning research in alternative medicine for healthcare professionals.
End-to-end autonomous driving models generate future trajectories from multi-view inputs, improving system integration but introducing opaque decisions and hard-to-localize risks. Existing methods either rely on auxiliary monitoring models or generate textual explanations, but are decoupled from the planning process and fail to reveal the visual evidence underlying trajectory generation. While att...
Medical multimodal large language models (MLLMs) have advanced image understanding and short-video analysis, but real clinical review often requires full-procedure video understanding. Unlike general long videos, medical procedures contain highly redundant anatomical views, while decisive evidence is temporally sparse, spatially subtle, and context dependent. Existing benchmarks often assume this ...
Large language models are increasingly deployed in clinical decision-support contexts, yet systematic evaluation of their factual reliability in gener...
Background and Objectives: Electrical stimulation mapping (ESM) is the clinical gold standard for identifying eloquent cortex during presurgical evalu...
Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectatio...
Accurate histological differentiation between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is critical for personalized treatment in non-sma...
Antiphospholipid syndrome (APS) lacks targeted therapies beyond anticoagulation, and its molecular heterogeneity remains poorly characterized. We empl...
Skin lesion classification is essential for early dermatological diagnosis, yet many existing computer-aided systems rely primarily on dermoscopic ima...
Long-term conversational memory requires retrieving evidence scattered across multiple sessions, yet single-pass retrieval fails on temporal and multi...
Document understanding is a critical capability in financial credit review, onboarding, and remote verification, where both decision accuracy and evid...
Current deepfake detection models achieve state-of-the-art performance on pristine academic datasets but suffer severe spatial attention drift under r...
Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging b...
Spatial omics technologies have revolutionized the molecular profiling of tissues but remain constrained by high costs and limited scalability. While ...
Capsule endoscopy (CE) enables non-invasive gastrointestinal screening, but current CE research remains largely limited to frame-level classification ...
Recent advances in deep research systems enable large language models to retrieve, synthesize, and reason over large-scale external knowledge. In medi...
Reinforcement learning (RL) post-training substantially improves remote sensing vision-language models (RS-VLMs). However, when handling complex remot...
Machine learning systems in fraud detection, credit scoring, and clinical risk assessment operate under delayed ground truth: outcome labels arrive da...
Multimodal remote sensing data provide complementary information for semantic segmentation, but in real-world deployments, some modalities may be unav...
Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) imaging, while essential fo...
Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful...