no code implementations • 20 Sep 2024 • Choonghan Kim, Seonhee Cho, Joo Heung Yoon
Purpose: To assess the robustness of LLMs in generating accurate impressions from chest radiography reports using both incomplete data and multimodal data.
no code implementations • 29 Apr 2024 • Seonhee Cho, Choonghan Kim, Jiho Lee, Chetan Chilkunda, Sujin Choi, Joo Heung Yoon
Recent advancements in Large Multimodal Models (LMMs) have attracted interest in their generalization capability with only a few samples in the prompt.
no code implementations • 24 Jan 2024 • Darren Liu, Cheng Ding, Delgersuren Bold, Monique Bouvier, Jiaying Lu, Benjamin Shickel, Craig S. Jabaley, Wenhui Zhang, Soojin Park, Michael J. Young, Mark S. Wainwright, Gilles Clermont, Parisa Rashidi, Eric S. Rosenthal, Laurie Dimisko, Ran Xiao, Joo Heung Yoon, Carl Yang, Xiao Hu
Methods: We investigated the performance of three general LLMs in understanding and processing real-world clinical notes.
no code implementations • 18 Jun 2022 • Arnab Dey, Mononito Goswami, Joo Heung Yoon, Gilles Clermont, Michael Pinsky, Marilyn Hravnak, Artur Dubrawski
Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML.