In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.

中文翻译：

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用于生物医学研究和应用的人工智能-类器官集成系统

在这篇综述中，我们探讨了人工智能 (AI) 在推进人类多能干细胞 (hPSC) 衍生类器官的生物医学应用方面日益增长的作用。干细胞衍生的类器官，这些微型器官复制品，已成为疾病建模、药物发现和再生医学的重要工具。然而，分析这些类器官生成的庞大而复杂的数据集可能效率低下且容易出错。人工智能技术提供了一种有前途的解决方案，可以有效地提取见解并从显微镜图像、转录组学、代谢组学和蛋白质组学生成的不同数据类型中进行预测。本综述简要概述了类器官的表征和人工智能的基本概念，同时重点全面探索了人工智能在基于类器官的疾病建模和药物评估中的应用。它为人工智能在增强类器官制造的质量控制、无标签类器官识别和复杂类器官结构的三维图像重建方面的未来可能性提供了见解。本综述提出了人工智能与类器官集成的挑战和潜在的解决方案，重点关注建立可靠的人工智能模型决策流程和类器官研究的标准化。