AcuLa aligns audio models with medical language models via contrastive and self-supervised objectives on LLM-generated clinical reports, raising mean AUROC from 0.68 to 0.79 across 18 cardio-respiratory tasks.
Respllm: Unifying audio and text with multimodal llms for generalized respiratory health prediction
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RespiraMFM reports 9.15% AUROC gain in supervised fine-tuning and 20.98% in zero-shot settings over baselines by aligning respiratory audio with clinical text across seven real-world datasets for five diseases.
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Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding
AcuLa aligns audio models with medical language models via contrastive and self-supervised objectives on LLM-generated clinical reports, raising mean AUROC from 0.68 to 0.79 across 18 cardio-respiratory tasks.
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RespiraMFM: A Multimodal Foundation Model with Contrastive Audio-Language Alignment for Respiratory Disease Identification
RespiraMFM reports 9.15% AUROC gain in supervised fine-tuning and 20.98% in zero-shot settings over baselines by aligning respiratory audio with clinical text across seven real-world datasets for five diseases.