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arXiv Computer Vision By Yi Qin, Marawan Elbatel, Shanshan Song, Taoran Huang, Jiarong Guo, Ningshu Li, Lehan Wang, Chenxu Zhao, Jiexin He, Machteld J. Boonstra, Stephan van der Zwaard, Marielle C. van de Veerdonk, Pere Lopez-Gutierrez, Lydia Dux-Santoy, Andrea Guala, Enrique Almar-Munoz, Sebastian Johannes Reinstadler, Radek Debiec, Yi-Hang Zhu, Huiyu Zhou, Adrian P Cheong, Yike Guo, Agnes Mayr, Karim Lekadir, Yong Jiang, Nilesh J. Samani, Dacheng Tao, Folkert W. Asselbergs, Hongwen Fei, Alex PW Lee, Xiaomeng Li

Multimodal Generative AI Assists Comprehensive Echocardiographic Interpretation

Read the original on arXiv Computer Vision →

EchoChat is a generative multimodal vision‑language model that performs comprehensive echocardiographic analysis, emulating a cardiologist’s cognitive workflow. Trained on 46,358 examinations, it achieves state‑of‑the‑art performance across 33 cardiac conditions, including 15 new ones, and outperforms existing foundation models in both recognition and quantification tasks. In a blinded reader study, reports co‑generated with EchoChat were rated higher in diagnostic accuracy and completeness, improving F1 score from 72.78% to 82.75%.

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 Computer Vision.

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