Latest AI and machine learning research in clinical trials for healthcare professionals.
Flexible, goal-directed behavior depends on the ability to select and prioritize information from memory representations freshly encoded from the sensory stream as well as retrieved from previous experience. The spatial gating signatures of internal attention in working memory (WM) are increasingly well characterized, but it remains unclear whether the same neurophysiological mechanisms are recrui...
The physical properties of biomolecular condensates, which form through phase separation, are central to their organisation and function and are increasingly implicated in disease mechanisms, including neurodegenerative disorders. To function correctly, condensates often recruit client biomolecules such as peptides and RNAs. This recruitment is not only essential to condensate biology but also off...
To make clinically grounded decisions, medical AI agents are expected to go beyond simple recognition and be capable of tool retrieval, evidence acqui...
Flow matching and diffusion models enable conditional generation across domains ranging from images to proteins, with recent extensions to out-of-dist...
The cost signal that constrained-RL algorithms optimize against is almost always reactive: the simulator emits a non-zero cost only after a collision ...
Background: This study aimed to evaluate real-world adverse event (AE) signals of EV to provide evidence-based guidance for its safe clinical applicat...
Quantized checkpoints are often screened first with quality metrics and only later, if at all, with direct safety tests. This paper audits that shortc...
End-of-rotation handoffs are critical for patient safety but add to documentation burden for hospitalists. Generative artificial intelligence (AI) may...
Diffusion transformers (DiTs) equipped with multimodal attention (MM-Attn) have become a dominant paradigm for image generation. However, preventing t...
PURPOSE: To develop and validate an artificial intelligence-enabled platform that converts unstructured cancer trial eligibility criteria into structu...
Background: Selective attention enables the prioritization of behaviorally relevant information in complex sensory environments. Despite substantial e...
Pathological gait datasets remain scarce due to privacy, recruitment, cost, and movement variability. Our work presents a multimodal LLM-guided framew...
Personalised cancer therapy aims to tailor treatment to individual tumour profiles, yet tumour heterogeneity and adaptive resistance continue to limit...
Abstract Background: Kaposi sarcoma (KS) is the most common cancer among men in several Eastern African countries, yet treatment monitoring relies on ...
Background: Major depressive disorder (MDD) is clinically heterogeneous, hindering identification of reproducible biomarkers. Using a semi-supervised ...
Background Endoscopic pituitary surgery involves navigating high-stakes anatomy where complications, such as carotid artery injury, cause devastating ...
Objective: Despite the complex and non-linear progression of diabetes, its shared pathways with atherosclerotic cardiovascular disease (ASCVD) are con...
Personalized oncology treatment recommendation is a critical clinical task that requires in-tegrating complex, multi-modal patient data with establish...
As large language models (LLMs) enter clinical workflows, automation bias, the uncritical acceptance of automated output, poses a patient-safety risk....
Conversational AI is being deployed into medical decision support, mental-health triage, and social companionship, where reinforcement of a user's fal...