Editing AI for Personalized Medicine (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Personalized Medicine AI Evidence Levels
! Application !! Evidence Level !! Regulatory Status !! Clinical Impact
|-
| Cancer targeted therapy matching || Very strong || FDA-approved CDx || High — standard of care
|-
| Pharmacogenomics (CYP2D6/2C19) || Strong || FDA table labeling || Moderate — uptake lagging
|-
| Immunotherapy biomarkers (TMB, MSI) || Strong || FDA-approved CDx || High
|-
| Polygenic risk scores (preventive) || Moderate || Research → clinical || Growing
|-
| Multi-omics subtyping || Moderate || Research || Emerging
|-
| Individual digital twins || Low-moderate || Research/trials || Early stage
|}

'''Failure modes''': Ancestry bias — pharmacogenomic and PRS models trained predominantly on European-ancestry populations. Actionability gap — many biomarkers predict outcomes but don't yet have matched treatments. Data silos — multi-omics integration requires data types that are rarely collected together clinically. Overfitting on small cohorts — personalization models trained on hundreds of patients may not generalize. Reimbursement barriers — payers don't cover many personalized diagnostics.
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