MedAI

Title: Towards Fundamental Biomedical AI: Integrating Vision, Language, and Signals

Speaker: Che Liu and Zhongwei Wan

Abstract:
This presentation showcases cutting-edge frameworks designed to address key challenges in biomedical AI, focusing on integrating vision-language and signal-language modalities for medical diagnostics. We introduce Med-UniC, an innovative solution for cross-lingual medical vision-language pre-training, effectively reducing community bias across languages to improve performance in medical image analysis. Further, we highlight the Multimodal ECG Representation Learning (MERL) framework, which advances zero-shot ECG classification by leveraging large language models and multimodal learning, and the Multimodal ECG Instruction Tuning (MEIT) framework, designed to automate ECG report generation. Both MEIT and MERL exhibit unparalleled performance in their fields, underlining the significant potential of multimodal and cross-lingual learning in enhancing biomedical AI through the seamless integration of clinical insights and artificial intelligence technologies.

Speaker Bio:
Che Liu, a 3rd-year PhD student at Imperial College London, is working on multimodal large language models (LLMs) for analyzing both daily world and healthcare data. Zhongwei Wan, a 1st-year student at Ohio State University, focuses on multimodal LLMs and enhancing the efficiency of LLMs.

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The MedAI Group Exchange Sessions are a platform where we can critically examine key topics in AI and medicine, generate fresh ideas and discussion around their intersection and most importantly, learn from each other.

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