ArticleJapanese journal of radiology2025
Application of NotebookLM, a large language model with retrieval-augmented generation, for lung cancer staging.
Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Large Language Models in Lung Cancer: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Evaluating Guideline Adherence in Gemini-Powered Dental Trauma Workflows: Standalone Gemini Chat vs. Document-Grounded NotebookLM.Dental traumatology : official publication of International Association for Dental Traumatology · 2026Article
- Percutaneous thermal ablation for renal cell carcinoma: a comprehensive review of current evidence and future directions.La Radiologia medica · 2026Review
- Evaluation Methods for Inference-Time Retrieval-Augmented and Graph Retrieval-Augmented Large Language Models in Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- Retrieval-Augmented Generation in Radiology: A Scoping Review of Architectures, Imaging Applications, and Directions for Equitable Deployment.Journal of imaging informatics in medicine · 2026Review
- Enhancing pancreatic cancer staging with large language models: the role of retrieval-augmented generation.Radiological physics and technology · 2026Article
- Automated RECIST tumor response classification through prompt-guided large language models.Scientific reports · 2026Article
- Feasibility of retrieval-augmented generation for large language models with Japanese input in radiotherapy.Journal of radiation research · 2026Article
- Evaluation of retrieval-augmented generation and large language models in clinical guidelines for degenerative spine conditions.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Retrieval-augmented generation-enhanced large language models for comprehensive CAD-RADS 2.0 categorization from structured coronary CTA reports.Radiologie (Heidelberg, Germany) · 2026Article
- Not Ready for Prime Time: Limitations of a Retrieval-Augmented Generation Large Language Model in Assessing Risk of Bias in Observational Studies.Journal of the Pediatric Orthopaedic Society of North America · 2026Article
- Evaluating the capability of large language models in radiotherapy through professional certification examinations in Japan.Journal of radiation research · 2026Article
- [Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- Reply: evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System (BI-RADS) Atlas 5Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
- Large Language Models for Drug-Related Adverse Events in Oncology Pharmacy: Detection, Grading, and Actioning.Pharmacy (Basel, Switzerland) · 2025Review
- A multi-criteria approach to ESG-based portfolio optimization incorporating historical performance, forward-looking insights, and credibilistic CVaR: a case study on the DJIA.Scientific reports · 2025Article
- Retrieval-augmented generation for answering Breast Imaging Reporting and Data System (BI-RADS)-related questions with large language models.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
purposeIn radiology, large language models (LLMs), including ChatGPT, have recently gained attention, and their utility is being rapidly evaluated. However, concerns have emerged regarding their reliability in clinical applications due to limitations such as hallucinations and insufficient referencing. To address these issues, we focus on the latest technology, retrieval-augmented generation (RAG), which enables LLMs to reference reliable external knowledge (REK). Specifically, this study examines the utility and reliability of a recently released RAG-equipped LLM (RAG-LLM), NotebookLM, for staging lung cancer. MATERIALS AND
methodsWe summarized the current lung cancer staging guideline in Japan and provided this as REK to NotebookLM. We then tasked NotebookLM with staging 100 fictional lung cancer cases based on CT findings and evaluated its accuracy. For comparison, we performed the same task using a gold-standard LLM, GPT-4 Omni (GPT-4o), both with and without the REK. For GPT-4o, the REK was provided directly within the prompt rather than through RAG.
resultsNotebookLM achieved 86% diagnostic accuracy in the lung cancer staging experiment, outperforming GPT-4o, which recorded 39% accuracy with the REK and 25% without it. Moreover, NotebookLM demonstrated 95% accuracy in searching reference locations within the REK.
conclusionNotebookLM, a RAG-LLM, successfully performed lung cancer staging by utilizing the REK, demonstrating superior performance compared to GPT-4o (without RAG). Additionally, it provided highly accurate reference locations within the REK, allowing radiologists to efficiently evaluate the reliability of NotebookLM's responses and detect possible hallucinations. Overall, this study highlights the potential of NotebookLM, a RAG-LLM, in image diagnosis.
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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.