A scalable framework for benchmark embedding models in semantic health-care tasks
By matching the appropriate model with the task, the framework enables more effective deployment of embedding models, enhancing critical applications such as semantic search and retrieval-augmented generation (RAG).
Authors
- Patricia Kovatch (ORCID: https://orcid.org/0000-0001-8368-1742)
- Benjamin Scott Glicksberg (ORCID: https://orcid.org/0000-0003-4515-8090)
- Eyal Klang (ORCID: https://orcid.org/0000-0002-4567-3108)
- Mahmud Omar (ORCID: https://orcid.org/0009-0001-0438-0827)
- Orly Efros (ORCID: https://orcid.org/0000-0002-6024-9110)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Robert Freeman (ORCID: https://orcid.org/0000-0003-4946-6533)
- Shelly Soffer (ORCID: https://orcid.org/0000-0002-7853-2029)
- Alexander W. Charney (ORCID: https://orcid.org/0000-0001-8135-6858)
- Moran Gendler
Institutions
- Mount Sinai Health System (US)
- Bar-Ilan University (IL)
- Tel Aviv University (IL)
- Sheba Medical Center (IL)
- Rabin Medical Center (IL)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-08-28
- DOI
- https://doi.org/10.1093/jamia/ocaf149
- Citations
- 1
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 2.48