  Large vision-language models (LVLMs), such as the Generative Pre-trained
Transformer 4-omni (GPT-4o), are emerging multi-modal foundation models which
have great potential as powerful artificial-intelligence (AI) assistance tools
for a myriad of applications, including healthcare, industrial, and academic
sectors. Although such foundation models perform well in a wide range of
general tasks, their capability without fine-tuning is often limited in
specialized tasks. However, full fine-tuning of large foundation models is
challenging due to enormous computation/memory/dataset requirements. We show
that GPT-4o can decode hand gestures from forearm ultrasound data even with no
fine-tuning, and improves with few-shot, in-context learning.
