The long-term vision behind Medical Variant Triage is not a machine that pronounces a genome understood. It is a system that carries raw genetic data through every deterministic and evidence-supported step that can be automated safely, then stops where interpretation genuinely begins.
The project takes the form of an umbrella design for normalizing genetic variants, collecting deterministic public evidence, applying explicit rules, and assembling reviewer-ready packets before handing uncertain questions to specialists. Its purpose is to make broad genetic analysis cheaper, more consistent, and easier for qualified reviewers to inspect.
The question behind Medical Variant Triage Methodology
That boundary changes the economics of the problem. Normalization, annotation, public evidence retrieval, inheritance-aware filtering, condition-specific workflows, provenance, and report assembly can consume expert time even when they do not require expert intuition at every step. Researchers, laboratory teams, clinicians, and ultimately people seeking genetic answers may benefit. Because errors could affect health decisions, the system is research infrastructure rather than a diagnostic service.
How Medical Variant Triage Methodology took shape
The methodology is being developed through interoperable parts: the Genome Model Runner, a manifest-first registry, public evidence adapters, structured workflow outputs, and the FH Variant Triage implementation. Each part is meant to expose its inputs, limits, and evidence rather than disappear into a single opaque score. Josiah defined the deterministic-first thesis, broad-access goal, safety boundary, and system architecture, then directed AI-assisted implementation and documentation.
What the evidence supports
Working research components can normalize prepared CSV or VCF inputs, query selected public evidence sources, and produce evidence tables, JSON, and reviewer packets. The ambition is broad access to genetic analysis, but the safety argument depends on restraint. Deterministic processing should make review cheaper and more complete; unresolved variants, conflicting evidence, and clinical meaning must remain visible instead of being smoothed into false certainty.
Coverage is incomplete, source assertions can conflict, and deterministic processing cannot replace clinical interpretation. No private genomes or personal health records are published. The next architectural stage would unify the runner, registry, and disease workflows behind a documented validation ladder, with expansion limited to independently reviewable adapters.