arXiv AI June 29, 2026 By Zuoou Li, Wenlong Zhao, Kelly Yu, Weitong Zhang, Paul M. Matthews, Wenjia Bai, Bernhard Kainz, Mengyun Qiao

CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association

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arXiv:2606. 28179v1 Announce Type: cross Abstract: Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research and reliable risk stratification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 2

CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations

arXiv:2606. 00123v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxies for clinical use, relying on isolated inputs and simplified recognition-style tasks.

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llms multimodal benchmarks

arXiv Machine Learning

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CARDINAL Predicts Cardiovascular Risk From Non-contrast Cardiac CT

arXiv:2608.27690v1 Announce Type: cross Abstract: Cardiovascular risk prediction remains limited by incomplete clinical data and imaging biomarkers that reduce computed tomography (CT) to a small num...

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arXiv AI

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CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping

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arXiv Machine Learning

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Flow-based conditional cardiac anatomy generation for virtual cohorts

arXiv:2608. 09460v1 Announce Type: new Abstract: Cardiac digital twin research is moving from subject-specific anatomical replicas toward virtual cohorts that represent clinically relevant population subgroups.

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arXiv AI

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arXiv Machine Learning

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

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By S M Asif Hossain, Ruksat Khan Shayoni, M. F. Mridha, Jungpil Shin

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Related stories

CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations

CARDINAL Predicts Cardiovascular Risk From Non-contrast Cardiac CT

CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping

Flow-based conditional cardiac anatomy generation for virtual cohorts

A Latent ODE Approach to Spatiotemporal Modeling of Cine Cardiac MRI

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