Drug Discovery's Blind Spot: Why Organoid-Artificial Intelligence Convergence Demands Integrated Governance.

Abstract

Organoid-artificial intelligence (AI) platforms are increasingly central to drug discovery, yet they sit between two governance regimes. AI frameworks presume stable, well-characterized inputs, while organoid ethics focuses on donor consent, moral status, and tissue use, leaving downstream computational uses of organoid-derived data underregulated. This article argues that the convergence of living biological variability with algorithmic decision-making creates a governance vacuum in high-stakes preclinical contexts. The organoid-AI case is distinctive because two mature governance traditions, built on incompatible assumptions, intersect at a decision-critical point, and because organoid-derived biological states are transient and often irrecoverable. The article maps the structural causes of this gap, illustrates its practical consequences through three failure scenarios assessing likely probability and impact, and proposes an integrated two-pillar framework: graduated AI validation indexed to organoid functional complexity and tamper-resistant audit trails linking biological and algorithmic metadata. It concludes that proactive governance is morally preferable to delayed, crisis-driven regulation.