Clinical reasoning from real-world oncology reports using large language models
This study shows that LLMs can perform complex guideline-based clinical reasoning using real-world radiology reports. By combining the RECIST/AJCC criteria with structured prompts, we demonstrated how LLMs can move beyond surface-level extraction to support nuanced inference in oncology, with implications for future clinical applications.
Authors
- Jaesung Heo (ORCID: https://orcid.org/0000-0003-2066-9062)
- Seonhwa Kim (ORCID: https://orcid.org/0000-0002-2949-066X)
- Jun Hyeong Park (ORCID: https://orcid.org/0000-0002-3051-4710)
Institutions
- Ajou University (KR)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251394622
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00