a16z addresses medical AI evaluation challenges; multimodal AI becomes biology's translation layer
agihouse_org · x · 2026-08-30
Medical AI faces an "oracle problem": subjective clinical judgments, opaque datasets, and marketing-driven evaluations make it hard for hospitals to objectively assess models. Partnering with Protege, a16z is addressing this by tracking patients' long-term outcomes.
Key takeaways from the AGI House blog post:
- Deeper definition of multimodality: In pharma, multimodal AI isn't just fusing images and text, but learning translation functions between biological scales (e.g., morphology to molecular state, cell trajectory to patient outcome).
- Different physical realities: Pathology images, transcriptomic profiles, and protein expression maps capture biology at different levels of organization.
- The frontier: The goal is not just fusing modalities, but learning how one level of biology can predict, explain, or simulate another.
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