activity
20192026
most citedMachine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

661 citations · 672 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Auditable CT Phenotyping Through Report-derived Radiological Observations

Riga Wu, Walter Witschey, Yicheng Li +9

Medical image foundation models can predict clinical phenotypes from computed tomography (CT), but strong performance leaves open whether they read disease-specific findings or sho…

cs.MA2026

ETHOS: Towards a Modular Ethics Framework for Clinical Multi-Agent Systems

Rakesh Sharma, Sydney Pugh, Cameron Beeche +14

The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patient data and supporting increasi…

cs.LG2024★ 4 cited

Evidence Is All You Need: Ordering Imaging Studies via Language Model Alignment with the ACR Appropriateness Criteria

Michael S. Yao, Allison Chae, Charles E. Kahn +4

Diagnostic imaging studies are an increasingly important component of the workup and management of acutely presenting patients. However, ordering appropriate imaging studies accord…

eess.IV2023★ 7 cited

A Generalizable Deep Learning System for Cardiac MRI

Rohan Shad, Cyril Zakka, Dhamanpreet Kaur +16

Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capab…

cs.LG2022

SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification

Michael S. Yao, Allison Chae, Matthew T. MacLean +8

Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable timely therapeutic interventions and lifestyle modifications. As the time available for clinical office visi…

cs.LG2019★ 661 cited

Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

Georgios Kissas, Yibo Yang, Eileen Hwuang +3

Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and…