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In recent years, machine learning, a powerful data analysis and modeling technique, is continuously revolutionizing the field of tissue engineering, and its ability to learn and extract information from complex datasets opens up new opportunities for the development of tissue engineering. In this paper, we first provide a categorized overview of different types of machine learning algorithms, and then focus on the recent advances in the application of machine learning in tissue engineering, summarize its latest applications in biomaterials, biomechanics, and biomanufacturing, and discuss the challenges faced and future prospects, with the aim of providing scientific references for researchers to achieve further progress in the fields of tissue engineering and machine learning. • This paper reviews the application of machine learning in the prediction, characterization, design, and optimization of biomaterial properties. In particular, it focuses on the study of ML in the intelligent response characteristics of biomaterials. • This paper outlines the application of machine learning in the biomechanics of tissue engineering scaffolds, aiming to improve the mechanical properties, accuracy, and adaptability of scaffolds to achieve personalized scaffold customization. • 3This paper describes advanced biomaterial manufacturing technologies based on machine learning, enabling the intelligence of biomaterial manufacturing processes and laying the foundation for accelerating the production of tissue engineering implants in the future. • This review thoroughly discusses the challenges faced by ML in tissue engineering applications, including data bottlenecks, ethical regulation, model interpretability, dynamic modeling, and clinical translation, and proposes future development directions and solutions.
Fu et al. (Wed,) studied this question.