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

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

For instance, Huang et al [19] demonstrated that providing LLMs with example outputs for few-shot learning and chain-of-thought reasoning methods for prompting yielded higher classification performance compared to baseline zero-shot applications of LLMs for data extraction. The careful design of prompting methodologies personalized to specific tasks and clinical domains within oncology may yield more accurate and efficient data extraction performance [49].

David Chen, Saif Addeen Alnassar, Kate Elizabeth Avison, Ryan S Huang, Srinivas Raman

JMIR Cancer 2025;11:e65984

Multidimensional Evaluation of the Process of Constructing Age-Friendly Communities Among Different Aged Community Residents in Beijing, China: Cross-Sectional Questionnaire Study

Multidimensional Evaluation of the Process of Constructing Age-Friendly Communities Among Different Aged Community Residents in Beijing, China: Cross-Sectional Questionnaire Study

Jo-Ying Huang et al [19] developed indicators of age-friendliness for communities in Taiwan province that conform to international standards by referring to the WHO’s Checklist and Taiwan’s existing indicators. Yu, Wong, and Woo [20] examined the relationships between perceptions of neighborhood environment, sense of community, and self-rated health by relying on the WHO Checklist. Wood et al [21] conducted a systematic scoping review of 23 articles using citizen science or participatory approaches.

Yingchun Peng, Zhiying Zhang, Ruyi Zhang, Yiyao Zhang, Runying Wang, Jiaying Zhang, Shaoqi Zhai, Qilin Jin, Jiaojiao Zhou, Jingjing Chen

JMIR Public Health Surveill 2025;11:e66248