Asclevor Research
Advancing intelligence for medicine.
Our research focuses on making medical knowledge machine-accessible: semantic retrieval over published clinical cases, embedding-based ranking, and careful, honest evaluation of what the systems can — and cannot — do.
Research areas
What we work on.
Clinical case retrieval
Retrieving clinically similar published cases from natural-language questions.
Medical knowledge representation
How clinical concepts, findings, and evidence relate to each other.
Embeddings & ranking
Embedding models and similarity measures for clinical text.
Evaluation
Measuring whether ranked results actually match clinical intent.
Technical work
Notes from the lab.
Retrieval
Measuring relevance in clinical case search
What does it mean for a retrieved case to be clinically similar? We define the criteria results are ranked against — and document how we measure them.
Embeddings
Embedding published cases at scale
More than 167,000 patient cases embedded with bge-small-en-v1.5. Notes on throughput, index growth, and what we look for in a model.
Evaluation
Honest evaluation for medical AI
Retrieval systems fail quietly. We document known limitations alongside measured strengths, and label everything still in progress.
Work in progress — notes are published as the work matures, with methods and limitations included.
Open at the core.
The foundation of Asclevor is a processed dataset of published patient cases drawn from the PubMed case literature. The database is open source — the API is the product.
- Cases processed
- 167,000+
- Cases indexed
- ~40,000
- Source literature
- PubMed case reports
- Source identifiers
- PMIDs retained
Responsible AI
Boundaries we hold.
- Clinical boundaries
- Asclevor supports knowledge discovery. It does not diagnose, recommend treatment, or replace clinical judgment.
- Traceability
- Every result points back to its published source through its PMID — no anonymous answers.
- No patient data
- The system is built on published literature. Patient records never enter the pipeline.
- Honest evaluation
- We report retrieval quality as it is measured today, and label what is still in progress.