Artificial Intelligence–Enabled Organoid Platforms for Precision Medicine: Integrating Multi-Omics, Digital Twins, and Microphysiological Systems

The convergence of artificial intelligence (AI) and organoid technology represents a transformative advance toward precision and predictive medicine. Organoids derived from pluripotent stem cells or patient tissues provide physiologically relevant three-dimensional models that recapitulate key aspects of native organ architecture and function. However, intrinsic biological heterogeneity, high-content imaging outputs, and dynamic spatiotemporal processes pose significant analytical challenges that exceed the capacity of conventional approaches. Recent advances in AI and machine learning enable automated image segmentation, quantitative morphometric profiling, and predictive modeling of organoid growth, differentiation, and therapeutic response, thereby enhancing reproducibility and translational relevance. The integration of multimodal datasets, including imaging, genomics, transcriptomics, epigenomics, proteomics, and metabolomics, has further enabled the development of organoid-based digital twins and in silico disease simulations to optimize personalized therapy. AI-enabled organoid-on-a-chip platforms, cloud-based analytics, and federated learning frameworks are accelerating the emergence of scalable, privacy-preserving, and data-driven biomedical ecosystems. Despite these advances, critical challenges persist, including data standardization, model interpretability, ethical governance, and clinical validation. In contrast to existing reviews that emphasize isolated AI applications, this study proposes a unified translational framework integrating AI-driven image analytics, multi-omics integration, digital twins, and organoid-on-a-chip systems within a precision medicine paradigm. By synthesizing current developments, methodological advances, and emerging trends, this study highlights how AI-powered organoid platforms can bridge experimental biology and clinical decision-making, with broad implications for drug discovery, disease modeling, and regenerative medicine. This review aims to provide a comprehensive overview of artificial intelligence–enabled organoid platforms by integrating advances in image analytics, multi-omics data integration, digital twins, and microphysiological systems, while highlighting their potential applications and future directions in precision medicine, drug discovery, and regenerative healthcare.