Use of SNOMED CT in Large Language Models: Scoping Review
This review demonstrates diverse approaches for integrating SNOMED CT into LLMs, with a focus on using concept descriptions to enhance biomedical language understanding and generation. While the results suggest potential benefits of SNOMED CT integration, the lack of standardized evaluation methods and comprehensive performance reporting hinders definitive conclusions about its effectiveness. Future research should prioritize consistent reporting of performance comparisons and explore more sophisticated methods for incorporating SNOMED CT's relational structure into LLMs. In addition, the biomedical NLP community should develop standardized evaluation frameworks to better assess the impact of ontology integration on LLM performance.
Authors
- Eunsuk Chang (ORCID: https://orcid.org/0000-0002-1350-3606)
- Sumi Sung (ORCID: https://orcid.org/0000-0003-3897-4698)
Institutions
- Chungbuk National University (KR)
- Korea Air Force Academy (KR)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2024-09-15
- DOI
- https://doi.org/10.2196/62924
- Citations
- 14
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 4.86