Editing AI for Clinical Trials (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Clinical Trial AI Impact by Phase
! Trial Phase !! AI Application !! Evidence of Benefit
|-
| All phases || Patient recruitment (EHR matching) || 50-70% faster recruitment (pilot data)
|-
| Phase I || Dose optimization (BOIN design) || Better dose finding with fewer patients
|-
| Phase II || Adaptive design || Higher graduation rate to Phase III
|-
| Phase III || Dropout prediction + prevention || 15-25% dropout reduction
|-
| Phase IV || Real-world safety signal detection || Earlier adverse event identification
|-
| All phases || Protocol deviation detection || 20-30% fewer GCP violations
|}

'''Failure modes''': EHR matching false positives — patients identified as eligible who don't actually meet criteria on chart review. Biased recruitment — AI models that identify patients from well-documented EHR systems over-recruit from academic medical centers, under-recruit from rural and minority communities. Overfitting adaptive designs — simulated advantages may not materialize in practice if simulation assumptions are wrong. Regulatory concerns — FDA scrutiny of AI-assisted adaptive designs requires rigorous pre-specification of adaptation rules.
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