Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review

Journals

1. Huang R, Sood T, Nelms M, Wintraub L, Leung F. Addressing the challenges of field notes in medical education: a qualitative study of resident experiences. BMC Medical Education 2025;25(1) View
2. Kamihara T, Omura T, Shimizu A. Potential and Limitations of Large Language Models for Medical Literature Analysis: A Preliminary Investigation. Cureus 2025 View
3. Kuno M, Osumi H, Udagawa S, Yoshikawa K, Ooki A, Shinozaki E, Ishikawa T, Oba J, Yamaguchi K, Sakurada K. Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery. Current Oncology 2025;32(11):588 View
4. Benson R, Kenny C, Ashraf Ganjouei A, Zhao M, Darawsheh R, Qian A, Hong J. Large Language Models in Population Oncology: A Contemporary Review on the Use of Large Language Models to Support Data Collection, Aggregation, and Analysis in Cancer Care and Research. JCO Clinical Cancer Informatics 2025;(9) View
5. Abumelha M, AL-Ghamdi A, Fayoumi A, Ragab M. Medical Feature Extraction from Clinical Exam Notes: Development and Evaluation of a Two-Phase Large Language Model Framework (Preprint). JMIR Medical Informatics 2025 View
6. Qiu Z, Jiang A, Qi C, Gan W, Zhu L, Mou W, Zeng D, Xiao M, Chu G, Peng S, Wong H, Zhang L, Zhang H, Deng X, Cheng Q, Tang B, Wang Y, Zhang J, Lin A, Luo P. Temporal evolution of large language models (LLMs) in oncology. Journal of Translational Medicine 2025;23(1) View
7. Culhane J. Use of a Novel Natural Language Processing Utility to Extract Structured Data From Free-Text Medical Notes. Cureus 2025 View
8. Luo I, Graber-Naidich A, Zhang M, Kaushik R, Nieda G, Chen T, Gu B, Choi E, Ding V, Gunturkun F, Satoyoshi M, Bhat A, Lee T, Su C, Ellis-Caleo T, Henry A, Desai M, Backhus L, Lui N, Leung A, Neal J, Kurian A, Langlotz C, Wakelee H, Liang S, Khan A, Han S. Leveraging large language models to extract smoking history from clinical notes for lung cancer surveillance. npj Digital Medicine 2025;8(1) View
9. May P, Greß J, Seidel C, Sommer S, Schuler M, Nokodian S, Schröder F, Jung J. Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data. JMIR Medical Informatics 2025;13:e78332 View
10. Capobianco I, Della Penna A, Mihaljevic A, Bitzer M, Eickhoff C, Stifini D. Clinical Accuracy and Safety Concerns Following GPT-5 Public Demonstration in Cancer Care. Journal of Medical Systems 2025;49(1) View
11. Kim S, Lee D, Kim Y, Yoon H, Lee H. Beyond Fine-Tuning: Leveraging Domain-Aware In-Context learning with large language models for clinical named entity recognition. Journal of Biomedical Informatics 2026:104982 View
12. Suri C, Ratre Y, Pande B, Bhaskar L, Verma H. Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine. World Journal of Gastroenterology 2026;32(1) View
13. Loaiza-Bonilla A, Thaker N, Tuysuz E, Yerdan S, Kurnaz S. Evaluating Multimodal LLMs for Information Extraction from Oncology Reports Requires a Clinically Curated Ground Truth—Two-Phase Evaluation of GPT-4.1 versus GPT-4.0. AI in Precision Oncology 2026 View
14. Hu X, Feng L, Jing B, Luo L, Tan W, Li Y, Zheng X, Huang X, Lin S, Wu H, He L. Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual Evaluation. Diagnostics 2026;16(2):215 View

Conference Proceedings

1. Tetz L, Capitaine L, Kobayashi R, Jagusch B, Nguyen T, Lux T. 2025 IEEE/ACS 22nd International Conference on Computer Systems and Applications (AICCSA). Integrating Gender-Sensitive Data into Clinical AI Systems: A Proof of Concept for Inclusive Healthcare View