Information Technology

Self-supervision drives representational convergence in medical foundation models more than clinical supervision

Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, what produces it, and whether it is clinically usable are untested, and the similarity measures behind such claims are fragile. We present a controlled dissection ...

SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework

Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted data sharing, client heterogeneity, class imbalance, and the lack of realistic tabular electronic health record (EHR) benchmarks. Synthetic data generation may alleviate data scarcity, yet its integration with federated optimisation has received limit...

Developing a Heart Failure Readmission Model From Inpatient Electronic Medical Record Data

Importance: Heart failure readmissions remain common following hospitalization, but accurately identifying which patients will be readmitted after dis...

From Pixel to Prognosis: Convolutional and GLCM Feature Fusion for Automated Four-Class Cataract Severity Classification

Objective: To develop a low-cost automated cataract severity classification system operating on standard consumer-grade colour photographs of the eye,...

Using binary silver labels in electronic health records-based computable phenotyping algorithms

Gold-standard phenotype labels are often unavailable at scale in electronic health record (EHR) studies because they require manual chart review. Weak...

A framework for human-artificial intelligence co-learning for disease activity labeling using electronic health records

Objective To develop and evaluate a framework for human-AI interaction. This approach, SHARE (Synergistic Human-Agent REasoning system) was designed t...

CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data

Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease ...

LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models

Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models...

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks

Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensiti...

Comorbidity Exposure-Window Definitions and Multidimensional Disparities in Long COVID Risk: Evidence from a U.S. National Cohort (2020-2024)

Long COVID (LC) affects millions of individuals worldwide, particularly those with preexisting comorbidities. However, whether these comorbidities sho...

AdaPCLA: Adaptive Prior-Calibrated Logit Adjustment for Long-Tailed Longitudinal EHR Generation

Generative modeling of longitudinal Electronic Health Records is increasingly important for privacy-preserving research, yet standard autoregressive m...

FHIRTrustBench: A Benchmark for Interoperability-Driven Clinical AI Readiness and Trustworthiness

Existing evaluations of healthcare AI often treat interoperability as a technical infrastructure issue rather than a factor that directly influences t...

Protocol for an EHR-embedded pragmatic randomized control trial of Ambient AI to Reduce Nursing Staff Documentation Time

Background: Documentation burden significantly impacts nursing workload and well-being, with nurses spending an estimated 20-40% of their time on docu...

NVAITC AI Scientist: A Governed End-to-End Research System -- A Hypertension GWAS Case Study

Agentic research systems are emerging as a new paradigm for coordinating scientific workflows beyond isolated model inference, code generation, or sta...

SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking

Motivation: Rare disease (RD) diagnosis is frequently delayed due to the similarities in symptoms to common disease variants. Machine Learning Algorit...

Automated Phenotypic Characterization in Rare Hematologic Malignancies Using a Large Language Model-Based Framework

Background. Diagnosis and risk stratification in rare hematologic malignancies such as myeloproliferative neoplasms (MPNs) - polycythemia vera (PV), e...

Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets

Privacy-preserving clustering is critical for analyzing sensitive data in healthcare, cybersecurity, and enterprise applications, where maintaining da...

Exploring the Application of the Observational Medical Outcomes Partnership Common Data Model to Multi-site Stroke Rehabilitation Research Data

Background: Emerging artificial intelligence and machine learning (AI/ML) tools can help generate robust knowledge to support precision rehabilitation...

The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology

- Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical rout...

Clinical Impact, Diagnostic Performance, and Prognostic Implications of Plasma Metagenomic Next-Generation Sequencing in Solid Organ Transplant Recipients

Introduction: Plasma metagenomic next-generation sequencing (mNGS) may detect pathogens in solid organ transplant (SOT) recipients, but optimal patien...