TY - JOUR
T1 - The Synergy of Artificial Intelligence and 3D Bioprinting
T2 - Unlocking New Frontiers in Precision and Tissue Fabrication
AU - Robazzi, Joao Vitor Silva
AU - Derman, Irem Deniz
AU - Gupta, Deepak
AU - Haugh, Logan
AU - Singh, Yogendra Pratap
AU - Pal, Vaibhav
AU - Yilmaz, Yasar Ozer
AU - Liu, Suihong
AU - Dias, Andre Luis
AU - Flauzino, Rogerio Andrade
AU - Ozbolat, Ibrahim Tarik
N1 - Publisher Copyright:
© 2025 The Author(s). Advanced Functional Materials published by Wiley-VCH GmbH.
PY - 2025
Y1 - 2025
N2 - This review examines the transformative role of artificial intelligence (AI) in 3D bioprinting, focusing on how advanced AI technologies enhance its precision, functionality, and scalability. AI, through branches, such as machine learning (ML), computer vision (CV), robotics, natural language processing (NLP), and expert systems (ES), provides critical improvements in real-time process monitoring, error correction, and optimization of bioprinting parameters. The integration of AI enables automated quality control and predictive maintenance, improving bioprinting outcomes by increasing cell viability and structural fidelity, and reducing the amount of bioink wasted. Specifically, ML algorithms are employed to predict optimal bioprinting conditions and streamline the bioprinting workflow, while deep learning enhances the ability to process complex datasets for precision tissue biofabrication. Furthermore, AI-powered robotics and CV systems ensure accurate bioink placement and facilitate the construction of complex tissues. Despite the remarkable progress, challenges remain, particularly in the areas of process monitoring, quality control, and the scalability of bioprinting systems. This review also aims to guide scientists, engineers, and healthcare providers in understanding the complexities and potential of AI-enhanced bioprinting, fostering a deeper appreciation of its role in the future of regenerative medicine and personalized healthcare.
AB - This review examines the transformative role of artificial intelligence (AI) in 3D bioprinting, focusing on how advanced AI technologies enhance its precision, functionality, and scalability. AI, through branches, such as machine learning (ML), computer vision (CV), robotics, natural language processing (NLP), and expert systems (ES), provides critical improvements in real-time process monitoring, error correction, and optimization of bioprinting parameters. The integration of AI enables automated quality control and predictive maintenance, improving bioprinting outcomes by increasing cell viability and structural fidelity, and reducing the amount of bioink wasted. Specifically, ML algorithms are employed to predict optimal bioprinting conditions and streamline the bioprinting workflow, while deep learning enhances the ability to process complex datasets for precision tissue biofabrication. Furthermore, AI-powered robotics and CV systems ensure accurate bioink placement and facilitate the construction of complex tissues. Despite the remarkable progress, challenges remain, particularly in the areas of process monitoring, quality control, and the scalability of bioprinting systems. This review also aims to guide scientists, engineers, and healthcare providers in understanding the complexities and potential of AI-enhanced bioprinting, fostering a deeper appreciation of its role in the future of regenerative medicine and personalized healthcare.
KW - 3d bioprinting
KW - artificial intelligence
KW - computer vision
KW - expert systems
KW - machine learning
KW - natural language processing
KW - robotics
UR - https://www.scopus.com/pages/publications/105019971243
U2 - 10.1002/adfm.202509530
DO - 10.1002/adfm.202509530
M3 - Review article
AN - SCOPUS:105019971243
SN - 1616-301X
JO - Advanced Functional Materials
JF - Advanced Functional Materials
ER -