A scalable framework for benchmark embedding models in semantic health-care tasks
Shelly Soffer, Mahmud Omar, Moran Gendler, Benjamin Scott Glicksberg, Patricia Kovatch, Orly Efros, Robert Freeman, Alexander W. Charney, Girish N. Nadkarni, Eyal Klang · Journal of the American Medical Informatics Association · 2025
OBJECTIVES: Text embeddings are promising for semantic tasks, such as retrieval augmented generation (RAG). However, their application in health care is underexplored due to a lack of benchmarking methods. We introduce a scalable benchmarking method to test embeddings for health-care semantic tasks. MATERIALS AND METHODS: We evaluated 39 embedding models across 7 medical semantic similarity tasks using diverse datasets. These datasets comprised real-world patient data (from the Mount Sinai Health System and MIMIC IV), biomedical texts from PubMed, and synthetic data generated with Llama-3-70b. We first assessed semantic textual similarity (STS) by correlating the model-generated similarity scores with noise levels using Spearman rank correlation. We then reframed the same tasks as retrieval problems, evaluated by mean reciprocal rank and recall at k. RESULTS: In total, evaluating 2000 text pairs per 7 tasks for STS and retrieval yielded 3.28 million model assessments. Larger models (>7b parameters), such as those based on Mistral-7b and Gemma-2-9b, consistently performed well, especially in long-context tasks. The NV-Embed-v1 model (7b parameters), although top in short tasks, underperformed in long tasks. For short tasks, smaller models such as b1ade-embed (335M parameters) performed on-par to the larger models. For long retrieval tasks, the larger models significantly outperformed the smaller ones. DISCUSSION: The proposed benchmarking framework demonstrates scalability and flexibility, offering a structured approach to guide the selection of embedding models for a wide range of health-care tasks. CONCLUSION: 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).