iLoRA introduces a novel Bayesian graph-conditioned LoRA framework that jointly learns prediction and latent interaction structure, significantly improving microbiome diagnosis by modeling microbe-mic…

The paper introduces Picid, a modular evaluation infrastructure that standardizes and formalizes the entire Prognostics and Health Management (PHM) evaluation pipeline to ensure reproducible and fair…

The paper introduces MedCase-Structured, a synthetic, FHIR-formatted dataset designed to benchmark diagnostic reasoning in realistic EHR settings, showing that LLMs perform worse on structured data th…

This paper introduces the FHIR Resource Access Graph (FRAG) to formally model and detect concurrency-related race conditions—such as Simultaneous Write Conflict and TOCTOU Authorization Violation—in h…

The paper proposes 'Think Fast, Talk Smart,' a pipeline that separates deterministic data analysis from LLM generation, showing that offloading recurring, structured tasks to code significantly improv…

UniD$^3$ is a novel Knowledge Graph-enhanced RAG framework that processes vast biomedical literature to systematically extract, organize, and validate comprehensive drug-disease knowledge, achieving h…

The paper introduces ClinEnv, a novel interactive, multi-stage benchmark designed to evaluate LLMs' decision-making and information-gathering process during longitudinal inpatient medical simulations.

The paper proposes GloResNet, a lightweight 3D CNN that effectively predicts brain injury in preterm infants using T2-weighted MRI, achieving an average accuracy of 75.18%.

The paper proposes a novel Generative Counterfactual Attention-guided Network (GCAN) that uses multimodal connectomes and brain atlas knowledge to provide explainable and highly accurate diagnosis of…

The paper introduces EHRBench, a large-scale, automated, and reliable benchmark derived from real Electronic Health Records (EHRs) to rigorously evaluate the clinical decision-making capabilities of L…

The paper introduces 'dashi,' an open-source Python library that provides comprehensive tools for characterizing dataset shifts (covariate, prior, concept) to ensure robust and trustworthy AI developm…

VulGD is a dynamic, open-access graph database that aggregates cybersecurity data from multiple sources and uses LLM embeddings to improve vulnerability representation and risk assessment.

C-MIG is a novel retrieval-augmented generation framework that uses multi-view information gain to improve clinical diagnosis reasoning by providing richer, more nuanced reward signals than existing m…

This paper proposes using genetic programming (GP) to jointly evolve both the feature sets and the structure of survival trees, resulting in highly interpretable and high-performing shallow models for…

The paper addresses 'Template Collapse' in 3D CT report generation—where models generate generic reports—by proposing CLarGen, a decoupled framework that significantly improves clinical accuracy and d…

The paper introduces the Hiremath Early Detection (HED) Score, a new measure-theoretic standard that accurately quantifies the time-value of early detection, significantly outperforming traditional me…

The paper proposes a causality-inspired multimodal Federated Domain Generalization framework to accurately classify respiratory sounds across different stethoscopes, overcoming the challenge of device…

This paper identifies prediction bias, a failure mode of entropy minimization in test-time adaptation, and proposes Distribution Shift Bias Reduction (DSBR) to stabilize adaptation and prevent model c…

The paper proposes a Bayesian meta-learner to accurately predict the distribution of Alzheimer's disease progression scores for individuals, outperforming existing methods, especially for long-term pr…