Fig. 1. (A) An example of zero-shot learning in a large language model. The upper panel shows a still-cut image of a poorly differentiated stomach adenocarcinoma. The zero-shot learning results (answers) were incorrect. The lower panel shows a still-cut image of submucosal invasion of early gastric cancer (adenocarcinoma). The zero-shot learning results (answers) were incorrect. This analysis was performed using ChatGPT 4 in January 2024. (B) An example of one-shot learning in a large language model. We trained the model using one representative image each of low-grade dysplasia, high-grade dysplasia, early gastric cancer, and advanced gastric cancer. The upper panel shows a still-cut image of low-grade dysplasia of the stomach, where the one-shot learning result was correct. The lower panel presents a still-cut image of advanced gastric cancer, with correct one-shot learning results. This analysis was performed using ChatGPT 4 in January 2024.

Fig. 2. A still cut image of a local PDF chat application developed using the Mistral 7-B large language model, Langchain, Ollama, and Streamlit (https://github.com/SonicWarrior1/pdfchat). The language model incorporates the concept of retrieval-augmented generation, which allows it to produce responses in the context of specific documents. It demonstrated appropriate question and answer capabilities after analyzing Chapter 321 of the 21st edition of Harrison’s Principles of Internal Medicine.

Fig. 3. A schematic view of a questionnaire system designed to facilitate communication between medical staff and patients (provided by Alexis Reality Co., Ltd.).

Fig. 4. An example of the analysis function in a large language model. ChatGPT 4 was accessed for this purpose in February 2024.