Question 1

What is AI in genomics?

Accepted Answer

AI in genomics uses machine learning and deep learning to analyze DNA and other omic data. Applications include variant calling, AI-assisted variant classification against ACMG and AMP criteria, VUS prioritization and re-analysis, functional effect prediction, and multi-omic modeling for precision medicine. It can accelerate interpretation while clinical sign-off remains with qualified experts.

Question 2

Does AI replace geneticists or variant interpreters?

Accepted Answer

No. AI can assemble and weigh evidence before a case reaches an interpreter, but qualified experts review, adjust, and sign off on clinical conclusions. It can reduce manual evidence-gathering work while preserving clinical judgment and accountability.

Question 3

How accurate is AI variant classification?

Accepted Answer

Accuracy depends on the gene, variant type, sequencing method, dataset, and clinical context. AlphaMissense has reported strong benchmark performance, while deep learning variant callers such as DeepVariant achieve high benchmark accuracy for SNP calling. AI outputs should be validated for the laboratory's intended use and reviewed with clinical and functional evidence.

Question 4

What are genomic foundation models?

Accepted Answer

Genomic foundation models are large neural networks pretrained on raw DNA sequences. Models such as Evo 2, Nucleotide Transformer, and DNABERT-2 learn patterns in genomic sequence data and can be adapted for tasks including variant-effect prediction, regulatory-element identification, and gene-expression prediction.

Question 5

Is AI variant interpretation HIPAA compliant?

Accepted Answer

AI-assisted variant interpretation can support HIPAA-aligned operations when the architecture includes appropriate safeguards such as isolated environments, encryption at rest and in transit, least-privilege access controls, PHI governance, immutable audit logs, and secure integration controls. Compliance depends on the full technical, administrative, and operational implementation.

Question 6

How do you build an AI genomics platform?

Accepted Answer

Build a governed data layer first, then add explainable AI-assisted classification, scheduled VUS re-analysis, validation against intended-use data, EHR and LIMS integration through standards such as FHIR R4, and ongoing model monitoring. The platform should also include security, provenance, versioning, auditability, and clinical review workflows.

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