46 citations · 62 across the 3 of their papers we have counts for
3 papers
Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation
Hisashi Johno, Yuki Johno, Akitomo Amakawa +9
Purpose: Retrieval-augmented generation (RAG) is a technology to enhance the functionality and reliability of large language models (LLMs) by retrieving relevant information from r…
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging
Ryota Tozuka, Hisashi Johno, Akitomo Amakawa +5
Purpose: In radiology, large language models (LLMs), including ChatGPT, have recently gained attention, and their utility is being rapidly evaluated. However, concerns have emerged…
Prediction-based compensation for gate on/off latency during respiratory-gated radiotherapy
Hisashi Johno, Masahide Saito, Hiroshi Onishi
During respiratory-gated radiotherapy (RGRT), gate on and off latencies cause deviations of gating windows, possibly leading to delivery of low- and high-dose radiations to tumors…