Abstract
Medical 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 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.
Overview
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CancerLLM is a 7-billion parameter language model designed specifically for oncology tasks, addressing limitations in existing medical language models.
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The model was pre-trained on a large dataset of cancer-related clinical notes and pathology reports, and fine-tuned for tasks such as cancer phenotype extraction, diagnosis generation, and treatment plan creation, achieving notable improvements in F1 scores.
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CancerLLM was evaluated for robustness against counterfactual data and misspellings, showing substantial resilience and practical applicability in clinical settings, with suggestions for future improvements in handling linguistic variants and expanding the dataset.
CancerLLM: A Large Language Model for Cancer Domain
The paper, "CancerLLM: A Large Language Model in Cancer Domain," introduces CancerLLM, a 7-billion parameter model architecture, specifically tailored for the cancer domain. The model addresses the limitations of existing medical LLMs such as ClinicalCamel 70B and Llama3-OpenBioLLM 70B, which, despite their impressive capabilities, lack specificity and efficiency when applied to oncology tasks.
Key Contributions and Methodology
The authors highlight several significant contributions through the development and evaluation of CancerLLM:
- Specialized Dataset and Pre-Training: CancerLLM was pre-trained using a substantial repository of cancer-related clinical notes and pathology reports, amounting to 2,676,642 clinical notes and 515,524 pathology reports. This data encompasses 17 types of cancer, thus providing a robust foundation for the model's cancer-specific training.
- Fine-Tuning for Downstream Tasks: Post pre-training, CancerLLM underwent fine-tuning for three cancer-relevant tasks—cancer phenotype extraction, cancer diagnosis generation, and cancer treatment plan generation. This targeted fine-tuning is crucial as it optimizes CancerLLM for specific, practical applications in the oncology field.
- Evaluation and Performance: The model's performance was benchmarked against 14 other LLMs, including 7B, 13B, and 70B parameter models. The evaluation metrics included Exact Match, BLEU-2, and ROUGE-L, with CancerLLM demonstrating a notable average F1 score improvement of 8.1% over existing models. This underscores its efficiency not only in terms of accuracy but also in computational resource management.
Robustness and Performance
To evaluate the robustness of CancerLLM, two testbeds were introduced: counterfactual robustness and misspellings robustness. These testbeds are critical as they assess the model's resilience to mislabeled data and linguistic inaccuracies, respectively.
- Counterfactual Robustness: The model exhibited strong robustness, especially at lower rates of counterfactual data. A substantial degradation in performance was observed only when the counterfactual rate exceeded 60%, indicating the model's resilience to a reasonable level of data noise.
- Misspellings Robustness: Here, CancerLLM maintained a slight edge in performance over other models at varying degrees of misspelling, though both the baseline and CancerLLM suffered significantly from high misspelling rates. This highlights the importance of high-quality, error-free data for optimal model performance.
Practical Implications and Future Developments
The implications of CancerLLM extend significantly into practical and theoretical realms:
- Enhanced Diagnostic and Treatment Capabilities: The adoption of a specialized model like CancerLLM in clinical settings can streamline the diagnosis and treatment plan generation processes. This, in turn, can potentially enhance patient care by providing more accurate and context-specific recommendations.
- Efficiency in Resource-Constrained Environments: With its relatively smaller parameter size (7B), CancerLLM is a more feasible option for deployment in medical institutions with limited computational resources compared to models with 13B or 70B parameters.
Limitations and Future Work
The analysis points out areas for further research:
- Handling Linguistic Variants: Future models could incorporate mechanisms for handling abbreviations, synonyms, and misspellings more effectively, thus improving robustness.
- Comprehensive Dataset Expansion: Increasing the diversity and volume of training data, especially for less-common cancer types, could further enhance model generalizability and accuracy.
Conclusion
In conclusion, CancerLLM represents a substantial advance in the application of LLMs to the oncology domain. By specializing its training and fine-tuning procedures, it achieves superior performance in cancer-specific tasks while maintaining computational efficiency. The proposed robust evaluation methodologies and insights into error cases offer a valuable roadmap for future enhancements. Consequently, CancerLLM holds significant promise for improving the landscape of AI-driven cancer diagnosis and treatment planning.
For interested researchers, this paper offers a compelling blend of innovative methodology, rigorous evaluation, and practical insights, forming a strong basis for future developments in clinical AI applications within oncology.
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