Zenith AI Company
Sector: Healthcare
Personalized Oncology via SLM & RAG
The Challenge: Oncology is the fastest-moving field in medicine. General-purpose LLMs hallucinate treatment dosages or cite outdated trials. Furthermore, genomic data is highly sensitive (PHI/PII), and hospitals are increasingly resistant to sending “Raw Sequence Data” to public cloud endpoints due to data sovereignty and latency concerns.
The Technical Solution
This architecture centers on a Sovereign Small Language Model (SLM)—a 7B to 14B parameter model (like Mistral or Llama-3-Oncology) fine-tuned on the “OncoKB” database and PubMed Central. This SLM is deployed on-premises using NVIDIA NIM microservices for optimized inference.
Advanced RAG Implementatio
We implement a Knowledge Graph RAG (KG-RAG). Traditional vector search misses the relationship between a gene mutation (e.g., BRAF V600E), a drug (Vemurafenib), and a specific metabolic pathway. The KG-RAG maps these entities, allowing the model to reason: “If the patient has Mutation X and has failed Treatment Y, then the next evidence-based step is Trial Z.” The retrieval layer uses Hybrid Search:
Dense Vectors
For semantic meaning (clinical notes).
Sparse BM25
For exact gene sequences and drug codes.
Reranker
A Cross-Encoder model that ranks the top 5 most relevant clinical trial matches.
MLOps & LLMOps Managed Services
Because medical knowledge changes daily, the LLMOps pipeline is critical. We implement Continuous Evaluators that test the model against a “Golden Dataset” of 500 oncology cases every week. If the model’s recommendation deviates from the “Standard of Care,” the deployment is rolled back, and the RAG index is updated.
Clinical Efficacy
30% increase in patients matched to life-saving clinical trials.
Physician Productivity
Reduces “Literature Search” time for complex cases by 4 hours per week.
Privacy
100% data residency; zero PHI leaves the hospital’s firewall.