Model-as-evidence framework established using AI-integrated organoids and organs-on-chips systems

The predictive capability crisis in drug discovery and development remains a significant factor hindering medical progress. Among emerging solutions, organoids offer the ability to reflect the biological fidelity of tissues, whereas organs-on-chips (OoCs) demonstrate strengths in simulating dynamic tissue microenvironments and enabling multi‑organ interactions. Herein, we present a unifying artificial intelligence (AI)-orchestrated framework that incorporates both platforms into verifiable and regulatable “model-as-evidence” (MAE) pathways capable of self-optimization with researcher oversight. AI is employed as an indispensable computational integration layer addressing three bottlenecks that render conventional approaches infeasible: (i) combinatorial explosion in high-dimensional parameter spaces, (ii) multi-scale coupling between molecular interactions and tissue-scale level morphogenesis, and (iii) requirement for real-time adaptive control of dynamic microenvironments exceeding human cognitive bandwidth. At the organoid level, this integration enables multi‑objective optimization to improve reproducibility, reduce resource use, and achieve predictive modeling to capture extracellular matrix–cells interactions. For OoCs, active learning algorithms condense protracted design cycles into efficient, goal-directed experimentation, and deep reinforcement learning sustains physiological steady states through adaptive, real-time control. The convergence of these systems within an integrated platform enables standardized, cross-comparable phenotyping pipelines that transform heterogeneous experimental data into a universal quantitative language, and harmonized evaluation benchmarks align experimental outputs across laboratories. Together, this framework generates curated evidence packages that directly address regulatory concerns related to efficacy and safety. By systematizing experimental design, parameter control, and interpretive analytics, this AI-orchestrated approach elevates organoid and OoC technologies from specialized crafts to a robust engineering discipline. It establishes a virtuous cycle wherein AI accelerates iterative validation and optimization, freeing researchers to focus on higher-order hypothesis generation and translational strategy. Thus, through AI-orchestrated quantification of biological fidelity and engineering rigor, these platforms may mature into credible, scalable, and transformative toolboxes for next-generation biomedical translation.