  Large language models (LLMs) have demonstrated potential in the innovation of
many disciplines. However, how they can best be developed for oncology remains
underdeveloped. State-of-the-art OpenAI models were fine-tuned on a clinical
dataset and clinical guidelines text corpus for two important cancer treatment
factors, adjuvant radiation therapy and chemotherapy, using a novel Langchain
prompt engineering pipeline. A high accuracy (0.85+) was achieved in the
classification of adjuvant radiation therapy and chemotherapy for breast cancer
patients. Furthermore, a confidence interval was formed from observational data
on the quality of treatment from human oncologists to estimate the proportion
of scenarios in which the model must outperform the original oncologist in its
treatment prediction to be a better solution overall as 8.2% to 13.3%. Due to
indeterminacy in the outcomes of cancer treatment decisions, future
investigation, potentially a clinical trial, would be required to determine if
this threshold was met by the models. Nevertheless, with 85% of U.S. cancer
patients receiving treatment at local community facilities, these kinds of
models could play an important part in expanding access to quality care with
outcomes that lie, at minimum, close to a human oncologist.
