ArticleNPJ digital medicine2026
CancerLLM: a large language model in cancer domain.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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Who cites it
19 citing papers in PubMed.
- The Rise of Small Language Models in Healthcare: A Comprehensive Survey.Computer science review · 2026Article
- Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction.Findings of ACL. ACL · 2026Article
- Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents.ESMO real world data and digital oncology · 2026Article
- Validating large language model-assisted data extraction from clinical notes.ESMO real world data and digital oncology · 2026Article
- Privacy-Preserving Generation of Structured Lymphoma Progression Reports from Cross-sectional Imaging: A Comparative Analysis of Llama 3.3 and Llama 4.Journal of imaging informatics in medicine · 2026Article
- The role of artificial intelligence in advancing population-based cancer registration.Science bulletin · 2026Review
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
- Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data.JMIR medical informatics · 2025Article
- Article
- Development and evaluation of large-language models (LLMs) for oncology: A scoping review.PLOS digital health · 2025Article
- A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology.Bioengineering (Basel, Switzerland) · 2025Article
- Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation.BMC medical imaging · 2025Article
- Framework for bias evaluation in large language models in healthcare settings.NPJ digital medicine · 2025Article
- Opportunities and challenges in lung cancer care in the era of large language models and vision language models.Translational lung cancer research · 2025Review
- Making shiny objects illuminating: the promise and challenges of large language models in U.S. health systems.npj health systems · 2025Article
- BiomedRAG: A retrieval augmented large language model for biomedicine.Journal of biomedical informatics · 2025Article
- Artificial intelligence in prostate cancer: navigating the new frontier of precision uro-oncology.American journal of clinical and experimental urology · 2025Review
- Leveraging Guideline-Based Clinical Decision Support Systems with Large Language Models: A Case Study with Breast Cancer.Methods of information in medicine · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Medical large language models (LLMs) perform well on medical NLP tasks, but lack models tailored for cancer phenotyping and diagnosis. Moreover, having tens of billions of parameters increases the computational burden in healthcare settings. To this end, we present CancerLLM, a 7-billion-parameter Mistral-style model trained on 2.7 M clinical notes and 515 K pathology reports across 17 cancer types, followed by fine-tuning on cancer phenotype extraction and diagnosis generation tasks. Our evaluation demonstrated that CancerLLM achieved strong performance on internal benchmarks, with F1 score of 91.78% on phenotyping extraction and 86.81% on diagnosis generation. It outperformed existing LLMs, with an average F1 score improvement of 9.23%. Additionally, the CancerLLM demonstrated its efficiency on time and GPU usage, and robustness comparing with other LLMs. We demonstrated that CancerLLM can potentially provide an effective and robust solution to advance clinical research and practice in cancer domain.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.