Editing AI for Genomic Medicine (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Genomic medicine AI operates in two main domains: **variant interpretation** (what does this genetic change mean for this patient?) and **clinical prediction** (what treatment will work, what disease will develop?).

**Variant interpretation with AI**: Human genome sequencing identifies millions of variants per patient. Clinical labs must classify which variants are responsible for disease. Manual interpretation using ACMG criteria is time-consuming and inconsistent. ML models trained on ClinVar, functional data, evolutionary conservation, and protein structure now predict variant pathogenicity with AUC >0.95 for well-studied gene-disease pairs. AlphaMissense (DeepMind, 2023) classified 89% of all possible human missense variants as likely pathogenic or benign — from 13% previously with confident classification.

**Pharmacogenomics in clinical AI**: Genetic variants in cytochrome P450 enzymes (CYP2D6, CYP2C19) dramatically affect drug metabolism. ML integrates pharmacogenomic data into clinical decision support: Genoptix, GeneSight, and hospital-embedded PGx AI systems recommend drug/dose adjustments based on patient genotype. CPIC (Clinical Pharmacogenomics Implementation Consortium) guidelines are being incorporated into EHR-embedded decision support.

**Precision oncology**: Tumor genomic profiling (Foundation One, MSK-IMPACT, Tempus xT) identifies actionable mutations (EGFR, BRCA1/2, ALK fusions) that match patients to FDA-approved targeted therapies. ML integrates tumor molecular profiles, treatment histories, and outcomes databases (AACR GENIE) to recommend therapies and predict response. The challenge: the space of tumor × drug combinations is vast, and randomized trial evidence covers only a small fraction.

**Multi-cancer early detection (MCED)**: Liquid biopsy AI tests (Galleri, CancerSEEK) detect cell-free tumor DNA in blood, potentially identifying cancers years before clinical presentation. They use ML on ctDNA methylation patterns, copy number changes, and fragmentomics to detect and locate tumors from a single blood draw. Galleri achieved 51% sensitivity at 99.5% specificity across 50+ cancer types in PATHFINDER trial.
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