Large language models for oncological applications

Abstract

Large language models such as ChatGPT have gained public and scientific attention. These models may support oncologists in their work. Oncologists should be familiar with large language models to harness their potential while being aware of potential dangers and limitations.

Fig. 1

The figure illustrates a flow of information from various input data sources (such as medical records, radiology and pathology reports, genomic data, clinical trial data, and scientific research) through large language model (LLM) processing, where tasks such as natural language understanding, pattern recognition, and data analysis are performed. Examples for LLM's applications in oncology are shown as output blocks, including text summarization, diagnosis and decision support, clinical trial matching, and prognostic prediction. Potential challenges and limitations of LLMs in oncology, such as data security concerns, biases, and legal responsibility are highlighted in black blocks