  In the past year, there has been a growing trend in applying Large Language
Models (LLMs) to the field of medicine, particularly with the advent of
advanced language models such as ChatGPT developed by OpenAI. However, there is
limited research on LLMs specifically addressing oncology-related queries. The
primary aim of this research was to develop a specialized language model that
demonstrates improved accuracy in providing advice related to oncology. We
performed an extensive data collection of online question-answer interactions
centered around oncology, sourced from reputable doctor-patient platforms.
Following data cleaning and anonymization, a dataset comprising over 180K+
oncology-related conversations was established. The conversations were
categorized and meticulously reviewed by field specialists and clinicians to
ensure precision. Employing the LLaMA model and other selected open-source
datasets, we conducted iterative fine-tuning to enhance the model's proficiency
in basic medical conversation and specialized oncology knowledge. We observed a
substantial enhancement in the model's understanding of genuine patient
inquiries and its reliability in offering oncology-related advice through the
utilization of real online question-answer interactions in the fine-tuning
process. We release database and models to the research community
(https://github.com/OncoGPT1).
