TY  - JOUR
AU  - Chen, David
AU  - Alnassar, Saif Addeen
AU  - Avison, Kate Elizabeth
AU  - Huang, Ryan S
AU  - Raman, Srinivas
PY  - 2025
DA  - 2025/3/28
TI  - Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review
JO  - JMIR Cancer
SP  - e65984
VL  - 11
KW  - artificial intelligence
KW  - chatbot
KW  - data extraction
KW  - AI
KW  - conversational agent
KW  - health information
KW  - oncology
KW  - scoping review
KW  - natural language processing
KW  - NLP
KW  - large language model
KW  - LLM
KW  - digital health
KW  - health technology
KW  - electronic health record
AB  - 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. 
SN  - 2369-1999
UR  - https://cancer.jmir.org/2025/1/e65984
UR  - https://doi.org/10.2196/65984
DO  - 10.2196/65984
ID  - info:doi/10.2196/65984
ER  -