  Medical Large Language Models (LLMs) such as ClinicalCamel 70B,
Llama3-OpenBioLLM 70B have demonstrated impressive performance on a wide
variety of medical NLP task.However, there still lacks a large language model
(LLM) specifically designed for cancer domain. Moreover, these LLMs typically
have billions of parameters, making them computationally expensive for
healthcare systems.Thus, in this study, we propose CancerLLM, a model with 7
billion parameters and a Mistral-style architecture, pre-trained on 2,676,642
clinical notes and 515,524 pathology reports covering 17 cancer types, followed
by fine-tuning on three cancer-relevant tasks, including cancer phenotypes
extraction, cancer diagnosis generation, and cancer treatment plan generation.
Our evaluation demonstrated that CancerLLM achieves state-of-the-art results
compared to other existing LLMs, with an average F1 score improvement of 8.1\%.
Additionally, CancerLLM outperforms other models on two proposed robustness
testbeds. This illustrates that CancerLLM can be effectively applied to
clinical AI systems, enhancing clinical research and healthcare delivery in the
field of cancer.
