ArticleNPJ precision oncology2024
Large language model use in clinical oncology.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed.
- Concordance between GPT-4 and a multidisciplinary tumor board in pancreatic cancer: A prospective pilot study.Langenbeck's archives of surgery · 2026Article
- Artificial intelligence-powered chatbots for oral anticancer drug patient information: an assessment of quality.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Article
- Evaluation of GPT-5, a Large Language Model, in Replicating German Clinical Practice Guideline Recommendations in Oral Oncology: A Cross-Sectional Concordance Study.Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology · 2026Article
- Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review.JMIR cancer · 2026Article
- Applications of Large Language Models in Ovarian Cancer Management: Protocol for a Systematic Review and Meta-Analysis.JMIR research protocols · 2026Article
- Single-session agreement of ChatGPT and Gemini treatment recommendations with multidisciplinary tumor board decisions in thoracic oncology.BMC cancer · 2026Observational
- Towards an ecosystem of clinical decision support tools for precision cancer medicine.NPJ precision oncology · 2026Review
- Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study.Journal of medical Internet research · 2026Article
- Digital Twin models to address long-term treatment toxicities in children and young adults with cancer.NPJ digital medicine · 2026Review
- ONCO-RADS-guided Large Language Models for Extraction and Classification of Incidental Findings on Whole-Body Imaging Reports.Radiology. Imaging cancer · 2026Article
- Evaluating open-source LLMs for dental EMR generation.BMC oral health · 2026Article
- Automated identification of radiotherapy treatment sites from unstructured physician notes.Journal of applied clinical medical physics · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- Cross-platform evaluation of LLM-generated educational texts on cardiac myxoma: quality, readability, and actionability using network analysis and latent profile analysis.Frontiers in cardiovascular medicine · 2026Article
- Precision oncology: from large language models to multi-agent systems.Frontiers in oncology · 2026Review
- Clinical evaluation of large language model recommendations in melanoma: comparison with multidisciplinary tumor board decisions in a real-world cohort.Frontiers in oncology · 2026Article
- Evaluation of an AI-assisted digital health follow-up system integrating humanistic care for patients undergoing chemotherapy: a prospective quasi-experimental study.Frontiers in public health · 2026Article
- A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification.Frontiers in systems biology · 2026Review
- Artificial Intelligence-Assisted Error Detection in Complex Clinical Documentation: Leveraging Large Language Models to Enhance Patient Safety in Oncology.JCO clinical cancer informatics · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) are undergoing intensive research for various healthcare domains. This systematic review and meta-analysis assesses current applications, methodologies, and the performance of LLMs in clinical oncology. A mixed-methods approach was used to extract, summarize, and compare methodological approaches and outcomes. This review includes 34 studies. LLMs are primarily evaluated on their ability to answer oncologic questions across various domains. The meta-analysis highlights a significant performance variance, influenced by diverse methodologies and evaluation criteria. Furthermore, differences in inherent model capabilities, prompting strategies, and oncological subdomains contribute to heterogeneity. The lack of use of standardized and LLM-specific reporting protocols leads to methodological disparities, which must be addressed to ensure comparability in LLM research and ultimately leverage the reliable integration of LLM technologies into clinical practice.
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.