Generation of guideline-based clinical decision trees in oncology using large language models

BackgroundMolecular biomarkers play a pivotal role in the diagnosis and treatment of oncologic diseases but staying updated with the latest guidelines and research can be challenging for healthcare professionals and patients. Large Language Models (LLMs), such as MedPalm-2 and GPT-4, have emerged as potential tools to streamline biomedical information extraction, but their ability to summarize molecular biomarkers for oncologic disease subtyping remains unclear. Auto-generation of clinical nomograms from text guidelines could illustrate a new type of utility for LLMs. MethodsIn this cross-sectional study, two LLMs, GPT-4 and Claude-2, were assessed for their ability to generate decision trees for molecular subtyping of oncologic diseases with and without expert-curated guidelines. Clinical evaluators assessed the accuracy of biomarker and cancer subtype generation, as well as validity of molecular subtyping decision trees across five cancer types: colorectal cancer, invasive ductal carcinoma, acute myeloid leukemia, diffuse large B-cell lymphoma, and diffuse glioma. ResultsBoth GPT-4 and Claude-2 "off the shelf" successfully produced clinical decision trees that contained valid instances of biomarkers and disease subtypes. Overall, GPT-4 and Claude-2 showed limited improvement in the accuracy of decision tree generation when guideline text was added. A Streamlit dashboard was developed for interactive exploration of subtyping trees generated for other oncologic diseases. ConclusionThis study demonstrates the potential of LLMs like GPT-4 and Claude-2 in aiding the summarization of molecular diagnostic guidelines in oncology. While effective in certain aspects, their performance highlights the need for careful interpretation, especially in zero-shot settings. Future research should focus on enhancing these models for more nuanced and probabilistic interpretations in clinical decision-making. The developed tools and methodologies present a promising avenue for expanding LLM applications in various medical specialties. Key Points- Large language models, such as GPT-4 and Claude-2, can generate clinical decision trees that summarize best-practice guidelines in oncology - Providing guidelines in the prompt query improves the accuracy of oncology biomarker and cancer subtype information extraction - However, providing guidelines in zero-shot settings does not significantly improve generation of clinical decision trees for either GPT-4 or Claude-2