Context
The project explores how an AI-assisted review workflow can help organize variant evidence without asking the user to blindly trust a generated answer.
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Case study
OncoTriage AI: provenance-first variant review
A genomic variant review copilot designed around explicit safety boundaries, evidence visibility, and failure handling.
Problem
In a sensitive domain, the hard part is not only calling a model. The workflow needs to show what evidence was retrieved, what the model inferred, and what failed or fell back.
Architecture
Evidence lookup and model scoring are separate paths. The UI presents provenance and mode state so users can distinguish retrieved evidence from generated interpretation.
Important decisions
I used explicit LIVE and DEMO modes, visible fallback behavior, and provenance-first output structure to avoid making the model appear more authoritative than the supporting data.
Failure modes handled
The system accounts for unavailable evidence, inference failure, and demo-mode execution by making those states visible instead of substituting silent placeholder answers.
Result
The project demonstrates my preferred pattern for applied AI: keep uncertainty visible, keep boundaries explicit, and design the interface around review rather than automation theater.
What I would improve next
The useful next step is hardening, not bigger claims.
- Add focused tests around evidence parsing and fallback states.
- Expand case-study screenshots once production-safe sample data is available.
- Tighten the review workflow with clearer confidence and source grouping.