60 citations · 60 across the 3 of their papers we have counts for
4 papers
CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation
Sonali Sharma, Jin Long, George Shih +7
Chest X-ray interpretation is one of the most frequently performed diagnostic tasks in medicine and a primary target for AI development, yet current vision-language models are prim…
Advances in LLM Reasoning Enable Flexibility in Clinical Problem-Solving
Kie Shidara, Preethi Prem, Jonathan Kim +4
Large Language Models (LLMs) have achieved high accuracy on medical question-answer (QA) benchmarks, yet their capacity for flexible clinical reasoning has been debated. Here, we a…
Limitations of Large Language Models in Clinical Problem-Solving Arising from Inflexible Reasoning
Jonathan Kim, Anna Podlasek, Kie Shidara +3
Large Language Models (LLMs) have attained human-level accuracy on medical question-answer (QA) benchmarks. However, their limitations in navigating open-ended clinical scenarios h…
EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation
Jonathan W. Kim, Ahmed Alaa, Danilo Bernardo
In conventional machine learning (ML) approaches applied to electroencephalography (EEG), this is often a limited focus, isolating specific brain activities occurring across dispar…