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arXiv AI By Ryo Takizawa, Tempei Kabayama, Chiemi Yamazaki, Shun Kitamura, Ryo Matsuoka, Yuta Ando, Satoshi Kodera, Norihiko Takeda

Integrating Heterogeneous Echocardiography AI Outputs in Language Space: Learnable Late Fusion with a Supervised LLM

Read the original on arXiv AI →

The paper presents a supervised large language model (LLM) that performs late fusion of heterogeneous echocardiography AI outputs directly in language space. By aggregating view‑specific predicted findings and measurements, the LLM learns a case‑level fusion function without requiring conversion to binary decisions or feature engineering. In a patient‑level three‑fold cross‑validation on 859 examinations, the method matched the best reference strategy and surpassed a general‑purpose LLM that lacked task‑specific training.

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.

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