Tissue engineering, a crucial approach in medical research and clinical applications, aims to regenerate damaged organs. By combining stem cells, biochemical factors, and biomaterials, it encounters challenges in designing complex 3D structures. Artificial intelligence (AI) enhances tissue engineering through computational modeling, biomaterial design, cell culture optimization, and personalized medicine. This review explores AI applications in organ tissue engineering (bone, heart, nerve, skin, cartilage), employing various machine learning (ML) algorithms for data analysis, prediction, and optimization. Each section discusses common ML algorithms and specific applications, emphasizing the potential and challenges in advancing regenerative therapies.
Impact Statement
This comprehensive review underscores the synergy between tissue engineering and AI, highlighting the transformative impact on regenerative therapies. By elucidating AI applications in organ-specific tissue engineering, the review emphasizes advancements in predicting scaffold performance, assessing cellular responses, and optimizing biomaterials. The integration of ML algorithms showcases promising outcomes, but challenges such as data quality, model interpretability, and standardization must be addressed for widespread implementation. The convergence of tissue engineering and AI holds immense potential for personalized and effective regenerative treatments, paving the way for future breakthroughs in medical science.
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