60 citations · 64 across the 4 of their papers we have counts for
5 papers
EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection
Sonali Santhosh, Kelly Shuhong Yu, Eugene Chang +3
Automated detection of interictal epileptiform discharges in scalp electroencephalography (EEG) is clinically important, but recent high-performing deep-learning models often trade…
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…
Simulation-based Inference of Developmental EEG Maturation with the Spectral Graph Model
Danilo Bernardo, Xihe Xie, Parul Verma +8
The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unkno…
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…