Latest AI and machine learning research in kidney transplantation for healthcare professionals.
Reject inference methods are widely used to mitigate survival bias in credit scoring, yet their effectiveness remains poorly understood. We systematically evaluate several such methods and uncover a structural failure mode: in a natural retraining cycle, models whose accuracy improves while recall collapses create an illusion of improvement that leads practitioners to believe the system is getting...
Translating high-dimensional, spatially resolved molecular datasets into testable biological findings remains a major research bottleneck. Here, we present OmicsNavigator, an autonomous large language model-powered system for end-to-end data exploration and hypothesis validation on spatial omics data. OmicsNavigator reasons directly over the multi-modal inputs of spatial omics data, including visu...
AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage. Although such systems p...
Clear cell renal cell carcinoma (ccRCC) exhibits pronounced heterogeneity across WHO histological grades, yet systematic single-cell multi-omics studi...
Background: Clinical LLM benchmarks rarely test whether algorithmic rankings agree with expert clinical judgment. We developed a trap-embedded periton...
A closed-loop quality system deployed across thirteen US hospital sites resolved physician complaints with zero regressions on 42 tracked cases across...
Female sexual behavior is fundamentally coupled to reproductive state, requiring the dynamic and coordinated regulation of sexual receptivity and reje...
Referring expression comprehension (REC) aims to localize a target object within an image based on a given expression. Although recent advances in vis...
Automated pavement distress assessment requires more than image-level classification or coarse bounding box detection, demanding precise localization ...
Light microscopy imaging with histological stains is central to disease diagnosis and research. It is enhanced with immunostaining to reveal cellular ...
We compare the efficacy and distributional effects of supervised fine-tuning (SFT) and reinforcement learning (RL) post-training for PlasmidGPT, a fou...
Large Language Models (LLMs) generate realistic synthetic data but offer no guarantee that their outputs respect the causal mechanisms governing the t...
Background: Previous machine learning models to intraoperatively predict the molecular status of gliomas using stimulated Raman histology (SRH), such ...
Background: Per- and polyfluoroalkyl substances (PFAS), particularly perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), are persisten...
We introduce a causal aware foundation-model framework for real time optimal decision making in discrete choice environments. We propose a constrained...
Manually curated biomedical repositories -- spanning bioactivity, genomics, and chemistry -- are expensive to maintain, lag behind primary literature,...
Mobile remote identity verification (RIdV) systems are exposed to attacks that manipulate or replace the facial video stream, including presentation a...
Identifying species in biology among tens of thousands of visually similar taxa while discovering unknown species in open-world environments remains a...
Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures in...
Multi-modal retrieval-augmented generation (MRAG) systems retrieve visual evidence from large image corpora to ground the responses of large multi-mod...