arXiv:2604.25605v2 Announce Type: replace-cross Abstract: Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keywords, offers advantages for retrieval of clinical information. However, deploying semantic search across health systems, comprising hundreds of millions of clinical notes, presents formidable engineering, cost, and governance challenges that have prevented institutional adoption. Methods: We deployed a semantic search system at a large children's hospital indexing 166 million clinical notes (484 million embedding vectors) from 1.68 mi
The deployment of semantic search across 166 million clinical notes at a major hospital demonstrates the increasing maturity and scalability of AI applications in highly sensitive data environments, pushing past previous engineering hurdles.
This development indicates a tangible progression in AI's ability to extract conceptual insights from vast unstructured clinical data, offering significant improvements for patient care, research, and operational efficiency within healthcare systems.
Semantic search can now be realistically implemented at a health system scale, moving beyond theoretical advantages to practical application, enabling real-time information retrieval based on meaning rather than keywords.
- · Healthcare providers
- · AI/ML developers
- · Patients
- · Medical researchers
- · Traditional keyword search vendors
- · Healthcare systems slow to adopt AI
Improved diagnostic accuracy and treatment planning through faster, more comprehensive data access for clinicians.
Acceleration of medical research by enabling researchers to quickly identify patterns and cohorts across massive clinical datasets.
The development of new AI-driven therapeutic strategies and personalized medicine protocols based on unprecedented data analysis capabilities.
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Read at arXiv cs.AI