Artificial intelligence-augmented organ- and organoid-on-a-chip for drug screening

Organ- and organoid-on-a-chip technologies provide critical human-relevant models for drug screening. Despite their promise, translating their complex biological outputs into reproducible, quantifiable, and pharmacologically interpretable readouts remains a significant challenge. To address this, artificial intelligence (AI) is increasingly employed to process the high-content imaging, sensor, and molecular data derived from these platforms. Crucially, this integration elevates AI from a conventional post-experimental analytical tool into a comprehensive framework that actively drives quality control, response quantification, model integration, and critical screening decisions. This review examines AI-augmented microphysiological systems across the entire drug screening pipeline by connecting biological readouts with specific computational strategies and pharmacological endpoints. We evaluate representative platforms, analytical methodologies, and specific applications where computational frameworks enable model standardization, robust phenotype interpretation, mechanism-informed evaluation, and compound prioritization. Furthermore, we outline primary barriers to clinical translation, including inherent biological and engineering variability, model generalizability, the need for external validation, and clinical dose relevance. Ultimately, these insights establish a comprehensive framework for evaluating the reproducibility, pharmacological applicability, interpretability, and translational potential of AI-driven microphysiological screening.