# EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation arXiv: https://arxiv.org/abs/2410.09704 ยท HTML: https://mlsanity.com/paper/2410.09704/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 32 references that are in the map. - First posted on arXiv 13 Oct 2024. ## Builds on (most cited first, 16 of 16) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022) - [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022) - [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021) - [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022) - [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023) - [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019) - [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017) - [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018) - [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021) - [FLAVA: A Foundational Language And Vision Alignment Model](https://mlsanity.com/paper/2112.04482/) (2021) - [BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs](https://mlsanity.com/paper/2303.00915/) (2023) - [Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing](https://mlsanity.com/paper/2301.04558/) (2023) - [Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks](https://mlsanity.com/paper/2211.09808/) (2022) - [Modeling Caption Diversity in Contrastive Vision-Language Pretraining](https://mlsanity.com/paper/2405.00740/) (2024) ## Built on by (most cited first, 18 of 18) - [FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis](https://mlsanity.com/paper/2502.14807/) (2025) - [EchoApex: A General-Purpose Vision Foundation Model for Echocardiography](https://mlsanity.com/paper/2410.11092/) (2024) - [How Well Can General Vision-Language Models Learn Medicine By Watching Public Educational Videos?](https://mlsanity.com/paper/2504.14391/) (2025) - [CardAIc-Agents: A Multimodal Framework with Hierarchical Adaptation for Cardiac Care Support](https://mlsanity.com/paper/2508.13256/) (2025) - [Video CLIP Model for Multi-View Echocardiography Interpretation](https://mlsanity.com/paper/2504.18800/) (2025) - [CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?](https://mlsanity.com/paper/2510.00520/) (2025) - [Automated Interpretable 2D Video Extraction from 3D Echocardiography](https://mlsanity.com/paper/2511.15946/) (2025) - [EchoVLM: Measurement-Grounded Multimodal Learning for Echocardiography](https://mlsanity.com/paper/2512.12107/) (2025) - [A Generative Framework for Bidirectional Image-Report Understanding in Chest Radiography](https://mlsanity.com/paper/2502.05926/) (2025) - [A Language-Signal-Vision Multimodal Framework for Multitask Cardiac Analysis](https://mlsanity.com/paper/2508.13072/) (2025) - [TREAT-Net: Tabular-Referenced Echocardiography Analysis for Acute Coronary Syndrome Treatment Prediction](https://mlsanity.com/paper/2509.23999/) (2025) - [Echo-CoPilot: A Multiple-Perspective Agentic Framework for Reliable Echocardiography Interpretation](https://mlsanity.com/paper/2512.09944/) (2025) - [Optimizing Point-of-Care Ultrasound Video Acquisition for Probabilistic Multi-Task Heart Failure Detection](https://mlsanity.com/paper/2602.13658/) (2026) - [MARCUS: An agentic, multimodal vision-language model for cardiac diagnosis and management](https://mlsanity.com/paper/2603.22179/) (2026) - [EchoSonar-R: A Multi-View Reasoning-Enabled Model for Disease Classification and Report Generation in Echocardiography](https://mlsanity.com/paper/2606.28164/) (2026) - [UltraPIPS: Improving model perception in B-mode ultrasound with foundation models](https://mlsanity.com/paper/2608.26033/) (2026) - [Reading the Whole Heart: Latent-Attention Masked Autoencoders for Multimodal Cardiac Representation Learning](https://mlsanity.com/paper/2609.12035/) (2026) - [Wrong Organ, Right Physics: Transferring Echocardiography Pretraining to Lung Ultrasound for Tuberculosis Screening](https://mlsanity.com/paper/2610.03290/) (2026) ## Abstract (arXiv) Echocardiography is the most widely used cardiac imaging modality, capturing ultrasound video data to assess cardiac structure and function. Artificial intelligence (AI) in echocardiography has the potential to streamline manual tasks and improve reproducibility and precision. However, most echocardiography AI models are single-view, single-task systems that do not synthesize complementary information from multiple views captured during a full exam, and thus lead to limited performance and scope of applications. To address this problem, we introduce EchoPrime, a multi-view, view-informed, video-based vision-language foundation model trained on over 12 million video-report pairs. EchoPrime uses contrastive learning to train a unified embedding model for all standard views in a comprehensive echocardiogram study with representation of both rare and common diseases and diagnoses. EchoPrime then utilizes view-classification and a view-informed anatomic attention model to weight video-specific interpretations that accurately maps the relationship between echocardiographic views and anatomical structures. With retrieval-augmented interpretation, EchoPrime integrates information from all echocardiogram videos in a comprehensive study and performs holistic comprehensive clinical echocardiography interpretation. In datasets from two independent healthcare systems, EchoPrime achieves state-of-the art performance on 23 diverse benchmarks of cardiac form and function, surpassing the performance of both task-specific approaches and prior foundation models. Following rigorous clinical evaluation, EchoPrime can assist physicians in the automated preliminary assessment of comprehensive echocardiography.