Applications of Large Language Models (LLMs) in Breast Cancer Care

Abstract

AbstractPurposeRecently introduced Large Language Models (LLMs) such as ChatGPT have already shown promising results in natural language processing in healthcare. The aim of this study is to systematically review the literature on the applications of LLMs in breast cancer diagnosis and care.MethodsA literature search was conducted using MEDLINE, focusing on studies published up to October 22nd, 2023, using the following terms: “large language models”, “LLM”, “GPT”, “ChatGPT”, “OpenAI”, and “breast”.ResultsFive studies met our inclusion criteria. All studies were published in 2023, focusing on ChatGPT-3.5 or GPT-4 by OpenAI. Applications included information extraction from clinical notes, question-answering based on guidelines, and patients’ management recommendations. The rate of correct answers varied from 64-98%, with the highest accuracy (88-98%) observed in information extraction and question-answering tasks. Notably, most studies utilized real patient data rather than data sourced from the internet. Limitations included inconsistent accuracy, prompt sensitivity, and overlooked clinical details, highlighting areas for cautious LLM integration into clinical practice.ConclusionLLMs demonstrate promise in text analysis tasks related to breast cancer care, including information extraction and guideline-based question-answering. However, variations in accuracy and the occurrence of erroneous outputs necessitate validation and oversight. Future works should focus on improving reliability of LLMs within clinical workflow.