Research
AI-assisted variant interpretation: what clinicians need to know
AI tools can accelerate variant triage, but clinical teams still need transparency, override paths, and workflow fit. Here is a practical framing for adoption in diagnostic labs.
Variant interpretation remains one of the highest-leverage steps in genomic diagnostics. AI-assisted systems can rank candidates, surface relevant literature, and highlight discordance between prediction engines and curated knowledge bases.
Clinicians should evaluate these tools on three dimensions: explainability, control, and integration. A ranked list without provenance is difficult to trust in sign-out. A black-box recommendation that cannot be edited breaks accountability.
The best deployments treat AI as a first-pass collaborator. Human reviewers retain final classification authority, with the system preserving an audit trail of what was suggested, accepted, or rejected.
Workflow fit determines adoption. Tools that require exports, duplicate data entry, or live outside the case record tend to stall. Embedding assistance inside the diagnostic case is what sustains daily use.
As labs scale WES and WGS, assisted interpretation will become baseline infrastructure. The clinical question is not whether to use AI, but how to ensure it strengthens—not replaces—expert judgment.