1 Artificial Intelligence to Discover and Create Innovative Biocomposites for Tissue Engineering Applications 1 In this commentary, we explore the role artificial intelligence (AI) can play in the development of innovative biocomposites for tissue engineering, emphasizing its ability to enhance material design and streamline production. Using artificial intelligence, biomaterials can be customized for individual patients, improving outcomes. AI-driven biocomposites are poised to transform tissue repair and regeneration, with personalized materials becoming standard by 2030 despite data quality and regulatory issues. 1 2 Mehdi Mohabbatkhah Department of Artificial Intelligence, Istinye University Turkey info@jourcc.com Darya Nejadkoorki M Kurs at Studienkolleg of the Frankfurt Goethe University Germany daryanejad2004@gmail.com Artificial Intelligence (AI) Tissue Engineering Biomedical Engineering Biocomposites Article2.pdf [1] Monfared, V., Application of Artificial Intelligence (Machine Learning) in Additive Manufacturing, Bio-Systems, Bio-Medicine, and Composites, in Additive Manufacturing for Biocomposites and Synthetic Composites. 2023, CRC Press. p. 152-203.##[2] Gao, W., et al., The status, challenges, and future of additive manufacturing in engineering. Computer-aided design, 2015. 69: p. 65-89.##[3] Dushyant, K., et al., Utilizing machine learning and deep learning in cybesecurity: an innovative approach. Cyber security and digital forensics, 2022: p. 271-293.##[4]Badini, S., S. Regondi, and R. Pugliese, Unleashing the power of artificial intelligence in materials design. Materials, 2023. 16(17): p. 5927.##[5] Kuhn, M., Applied predictive modeling. 2013, Springer.##[6] Al Mahmud, M.Z., Exploring the versatile applications of biocomposites in the medical field. Bioprinting, 2023. 36: p. e00319.##[7] Haleem, A., et al., Hyperautomation for the enhancement of automation in industries. Sensors International, 2021. 2: p. 100124.##[8] Joyce, K., et al., Bioactive potential of natural biomaterials: Identification, retention and assessment of biological properties. Signal transduction and targeted therapy, 2021. 6(1): p. 122.##[9] Al-Kharusi, G., et al., The role of machine learning and design of experiments in the advancement of biomaterial and tissue engineering research. Bioengineering, 2022. 9(10): p. 561.##[10] Neftci, E.O. and B.B. Averbeck, Reinforcement learning in artificial and biological systems. Nature Machine Intelligence, 2019. 1(3): p. 133-143.##[11]Kothuru, S.K., AI-Driven Innovations in Healthcare: Improving Diagnostics and Patient Care. International Journal of Machine Learning and Artificial Intelligence, 2023. 4(4): p. 1-13