Latest AI and machine learning research in prescriptions for healthcare professionals.
Multi-vector vision-language retrieval preserves fine-grained visual evidence through maximum-similarity late interaction, but dense image-side tokens make storage and scoring expensive. Existing token compression methods reduce this cost, yet they can remove or collapse object- and region-level evidence that future query tokens may need to select. We propose SaMer, an object-aware token merging f...
Large Language Models (LLMs) can produce detailed answers to complex queries, but these answers are typically presented as dense linear text, which makes fine-grained inspection, navigation, and return visits difficult. We present ChatImage, a system that converts long-form LLM answers into interactive visual images. Given a textual answer, ChatImage first normalizes its content into structured vi...
While Large Vision-Language Models (VLMs) demonstrate remarkable generic capabilities, their clinical reasoning in specialized domains like ocular sur...
Background: Tuberculosis, especially drug-resistant tuberculosis (DR-TB) including multidrug-resistant (MDR) and extensively drug-resistant (XDR) stra...
Although DNA Large Language Models (DNA-LLMs) offer a path to decoding genetic complexity, our ability to evaluate these models is constrained by our ...
BackgroundHealth care organizations are increasingly required to make strategic decisions about artificial intelligence (AI) systems before their clin...
Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development. A difficul...
Multi-subject personalized image generation requires the precise rendering of all requested reference identities and their specified interactions base...
Background: Polypharmacy is common in people living with dementia (PLwD) and associated with adverse outcomes. Although Structured Medication Reviews ...
Background: To develop and validate multiple Machine Learning (ML) algorithms that predict Mechanical Ventilation (MV) requirement in Guillain-Barre S...
Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managi...
Multiple learning signals can shape motor output, including reward and punishment (via value-based reinforcement learning) and sensorimotor error (via...
Background: Standardized evaluation of agentic artificial intelligence (AI) for medication management is lacking. Given the potential lethality of med...
Background Large Language Models (LLMs) are increasingly explored for pharmacovigilance tasks, including information extraction, case documentation, a...
We applied the model-free GenericML framework using robust estimators (the Best Linear Predictor, Group Average Treatment Effects, and baseline profil...
Urban building change detection from bi-temporal aerial imagery is important for redevelopment monitoring, infrastructure management, and unauthorized...
Medication errors, particularly dosing errors in clinical trials (CT), can lead to patient harm, adverse drug events and worse patient outcomes. Dosin...
Over the past decade, interest in applying machine learning (ML) to automate forest monitoring has grown significantly. However, existing training dat...
Positron Emission Tomography (PET) reveals brain metabolism and is clinically central to neurodegenerative disease assessment, yet existing 3D brain f...
Objective: Electronic health record (EHR) audit logs capture clinician-EHR interaction patterns, but most audit log research relies on aggregated meas...