Challenges We Faced Building RAG for Genetic Report Interpretation
Genetic reports contain a huge amount of valuable information, but they are often difficult for patients to understand. We provide over 2000 pages to customer as their genetic test result!
They have complex medical terminology, long explanations, and inconsistent formatting.
For that reason, we started exploring how Retrieval Augmented Generation(RAG) could help users better understand their reports while still keeping the system reliable and clinically cautious.
Why Simple AI Summarization Was Not Enough
At first, using a large language model to summarize reports seemed straightforward. But in practice, we quickly discovered several problems.
Miss important medical context
Long PDF reports were difficult to process consistently
Medical AI systems cannot behave like generic chat-bots. Small mistakes in interpretation can create confusion for users.
With this context, we chose to use a more structured RAG-based approach.'
Only Early RAG Pipeline
Our current workflow focuses on:
Extracting report content
Splitting documents into searchable chunks
Retrieving relevant medical context
Generating assistive explanations using AI models
Instead of replacing medical professionals, the goal is to help users navigate complicated information more clearly.
Technical Challenges We Encountered
Some of the biggest engineering challenges included
- Inconsistent Document Structures
Different clinics and labs format reports differently, making reliable parsing difficult.
- Retrieval Noise
Even small retrieval mistakes could lead to irrelevant or incomplete explanations.
- Medical Terminology
Genetic terminology is highly specialized.
- Privacy and Compliance ⭐
Because health data is sensitive, infrastructure decisions also needed to consider compliance requirements as HIPPA, PIPEDA, and Quebec Law 25.
One of the biggest lessons so far is that healthcare AI systems require much more than simply connecting a language model to a database.
Reliability, retrieval quality, explain-ability, and careful wording matter just as much as model performance.
We are continuing to improve :
retrieval accuracy
structured medical context handling
multilingual support
FHIR-based interoperability
overvaluation and monitoring workflows
This is only the beginning of our journey, and we plan to share more lessons as Ebovir continues building reliable AI systems for healthcare.