ArticleJMIR cancer2025
Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review.
Article in JMIR cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Hierarchical prompting with reasoning large language models for immune checkpoint inhibitor-associated myocarditis evidence extraction pilot study.European heart journal. Digital health · 2026Article
- Augmenting structured diagnoses through effective use of pre-trained large language models on clinical notes.JAMIA open · 2026Article
- Large Language Models for Clinical Data Extraction in Workplace Injury Rehabilitation: Protocol for a Retrospective Pilot Study of Accuracy, Fairness, and Methodological Considerations in Workers' Compensation Medical Chart Review.JMIR research protocols · 2026Article
- Large Language Models in Multidisciplinary Decision-Making for Hepatopancreatobiliary Oncology: Retrospective Comparative Feasibility Study.Journal of medical Internet research · 2026Article
- Large language models in oncology: promise, pitfalls, and the path to real-world adoption.ESMO real world data and digital oncology · 2026Article
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Article
- AI-Assisted Clinical Data Abstraction From Electronic Health Records: Retrospective Concordance Study.JMIR formative research · 2026Article
- Automated identification of keratinocyte cancers in pathology reports using large language models.PLOS digital health · 2026Article
- Large language models for cancer registry abstraction: a real-world evaluation across models, variables, and cancer types.medRxiv : the preprint server for health sciences · 2026Article
- Evaluation of Large Language Models for Structured Data Extraction From Interstitial Lung Disease Clinical Notes: Comparative Study.Journal of medical Internet research · 2026Article
- A Pilot Project Leveraging Large Language Models for Automated Screening and Variable Extraction in Observational Studies.medRxiv : the preprint server for health sciences · 2026Article
- Retrospective Cohort Study: Extracting Coexisting Background Breast-Lesion Features from Stage I-III Invasive Breast Cancer.Cancers · 2026Article
- Digital Registrar: A Schema-First Framework for Multi-Cancer Privacy-Preserving Pathology Abstraction via Local LLMs.Diagnostics (Basel, Switzerland) · 2026Article
- 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
- A workflow utilizing general-purpose large language models for efficient structuring and data mining of bone scintigraphy narratives.Scientific reports · 2026Article
- Review
- From LLMs to AI agents: a systematic benchmark for SAO structure extraction in patent analytics.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Natural language processing systems for data extraction from unstructured clinical text require expert-driven input for labeled annotations and model training. The natural language processing competency of large language models (LLM) can enable automated data extraction of important patient characteristics from electronic health records, which is useful for accelerating cancer clinical research and informing oncology care. Objective: This scoping review aims to map the current landscape, including definitions, frameworks, and future directions of LLMs applied to data extraction from clinical text in oncology. Methods: We queried Ovid MEDLINE for primary, peer-reviewed research studies published since 2000 on June 2, 2024, using oncology- and LLM-related keywords. This scoping review included studies that evaluated the performance of an LLM applied to data extraction from clinical text in oncology contexts. Study attributes and main outcomes were extracted to outline key trends of research in LLM-based data extraction. Results: The literature search yielded 24 studies for inclusion. The majority of studies assessed original and fine-tuned variants of the BERT LLM (n=18, 75%) followed by the Chat-GPT conversational LLM (n=6, 25%). LLMs for data extraction were commonly applied in pan-cancer clinical settings (n=11, 46%), followed by breast (n=4, 17%), and lung (n=4, 17%) cancer contexts, and were evaluated using multi-institution datasets (n=18, 75%). Comparing the studies published in 2022-2024 versus 2019-2021, both the total number of studies (18 vs 6) and the proportion of studies using prompt engineering increased (5/18, 28% vs 0/6, 0%), while the proportion using fine-tuning decreased (8/18, 44.4% vs 6/6, 100%). Advantages of LLMs included positive data extraction performance and reduced manual workload. Conclusions: LLMs applied to data extraction in oncology can serve as useful automated tools to reduce the administrative burden of reviewing patient health records and increase time for patient-facing care. Recent advances in prompt-engineering and fine-tuning methods, and multimodal data extraction present promising directions for future research. Further studies are needed to evaluate the performance of LLM-enabled data extraction in clinical domains beyond the training dataset and to assess the scope and integration of LLMs into real-world clinical environments.
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