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CC BY 4.0 · Yearb Med Inform 2024; 33(01): 090-098
DOI: 10.1055/s-0044-1800726
Section 2: Cancer Informatics
Survey

A Narrative Review on the Application of Large Language Models to Support Cancer Care and Research

Ryzen Benson
1   Department of Radiation Oncology, University of California, San Francisco, San Francisco, California
2   Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California
,
Marianna Elia
1   Department of Radiation Oncology, University of California, San Francisco, San Francisco, California
2   Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California
,
Benjamin Hyams
1   Department of Radiation Oncology, University of California, San Francisco, San Francisco, California
2   Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California
3   School of Medicine, University of California, San Francisco, San Francisco, California
,
Ji Hyun Chang
1   Department of Radiation Oncology, University of California, San Francisco, San Francisco, California
2   Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California
4   Department of Radiation Oncology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
,
Julian C. Hong
1   Department of Radiation Oncology, University of California, San Francisco, San Francisco, California
2   Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California
5   UCSF UC Berkeley Joint Program in Computational Precision Health (CPH), San Francisco, CA
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Summary

Objectives: The emergence of large language models has resulted in a significant shift in informatics research and carries promise in clinical cancer care. Here we provide a narrative review of the recent use of large language models (LLMs) to support cancer care, prevention, and research.

Methods: We performed a search of the Scopus database for studies on the application of bidirectional encoder representations from transformers (BERT) and generative-pretrained transformer (GPT) LLMs in cancer care published between the start of 2021 and the end of 2023. We present salient and impactful papers related to each of these themes.

Results: Studies identified focused on aspects of clinical decision support (CDS), cancer education, and support for research activities. The use of LLMs for CDS primarily focused on aspects of treatment and screening planning, treatment response, and the management of adverse events. Studies using LLMs for cancer education typically focused on question-answering, assessing cancer myths and misconceptions, and text summarization and simplification. Finally, studies using LLMs to support research activities focused on scientific writing and idea generation, cohort identification and extraction, clinical data processing, and NLP-centric tasks.

Conclusions: The application of LLMs in cancer care has shown promise across a variety of diverse use cases. Future research should utilize quantitative metrics, qualitative insights, and user insights in the development and evaluation of LLM-based cancer care tools. The development of open-source LLMs for use in cancer care research and activities should also be a priority.

Keywords

large language models - clinical decision support - artificial intelligence - natural language processing - generative AI


Publikationsverlauf

Artikel online veröffentlicht:
08. April 2025

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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