Large Language Model Versus Manual Review for Clinical Data Curation in Breast Cancer: Retrospective Comparative Study
LLM-based curation of automatically extracted, deidentified clinical data demonstrated comparable effectiveness to manual physician review while reducing processing time by 95% and physician hours by 91%. This 2-step approach-automated data extraction followed by LLM curation-addresses both privacy concerns and efficiency needs. Despite limitations in integrating multiple clinical events, this methodology offers a scalable solution for clinical data extraction in oncology research. The 90.8% accuracy rate and superior capture of survival events suggest that combining automated data extraction systems with LLM processing can accelerate retrospective clinical research while maintaining data quality and patient privacy.
Authors
- Chang Ik Yoon (ORCID: https://orcid.org/0000-0002-7273-8886)
- Jiyoung Rhu (ORCID: https://orcid.org/0000-0002-9173-5445)
- Jong Min Baek (ORCID: https://orcid.org/0000-0001-5260-3821)
- Hocheol Lee (ORCID: https://orcid.org/0000-0003-1467-8843)
- H.S. Kim (ORCID: https://orcid.org/0009-0002-9738-6554)
- Young-Joon Kang (ORCID: https://orcid.org/0000-0002-6546-0513)
- Ye Won Jeon (ORCID: https://orcid.org/0000-0002-5181-8211)
- Yong‐Seok Kim (ORCID: https://orcid.org/0000-0003-4894-5006)
- Hoon Choi (ORCID: https://orcid.org/0000-0002-8622-4800)
- Jae Pak Yi (ORCID: https://orcid.org/0009-0007-3239-6459)
- Sueng-Bum Lim (ORCID: https://orcid.org/0009-0000-2761-3980)
Institutions
- Yonsei University (KR)
- St. Mary's Hospital (US)
- The Catholic University of Korea St. Vincent's Hospital (KR)
- The Catholic University of Korea Incheon St. Mary's Hospital (KR)
- The Catholic University of Korea Uijeongbu St. Mary's Hospital (KR)
- The Catholic University of Korea Yeouido St. Mary's Hospital (KR)
- The Catholic University of Korea Bucheon St. Mary's Hospital (KR)
- The Catholic University of Korea Seoul St. Mary's Hospital (KR)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-11-06
- DOI
- https://doi.org/10.2196/73605
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 4.96