AI-driven 3D bioprinting for regenerative medicine: From bench to bedside
In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to be...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Bioactive Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2452199X2400505X |
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| author | Zhenrui Zhang Xianhao Zhou Yongcong Fang Zhuo Xiong Ting Zhang |
| author_facet | Zhenrui Zhang Xianhao Zhou Yongcong Fang Zhuo Xiong Ting Zhang |
| author_sort | Zhenrui Zhang |
| collection | DOAJ |
| description | In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting. |
| format | Article |
| id | doaj-art-78375fd69e0743828842d3848c035ff5 |
| institution | Kabale University |
| issn | 2452-199X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Bioactive Materials |
| spelling | doaj-art-78375fd69e0743828842d3848c035ff52025-01-26T05:04:23ZengKeAi Communications Co., Ltd.Bioactive Materials2452-199X2025-03-0145201230AI-driven 3D bioprinting for regenerative medicine: From bench to bedsideZhenrui Zhang0Xianhao Zhou1Yongcong Fang2Zhuo Xiong3Ting Zhang4Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China; Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, PR China; “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base (111 Base), Beijing, 100084, PR ChinaBiomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China; Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, PR China; “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base (111 Base), Beijing, 100084, PR ChinaBiomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China; Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, PR China; “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base (111 Base), Beijing, 100084, PR China; State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, 100084, PR China; Corresponding author. Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China.Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China; Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, PR China; “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base (111 Base), Beijing, 100084, PR China; Corresponding author. Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China.Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China; Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, PR China; “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base (111 Base), Beijing, 100084, PR China; State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, 100084, PR China; Corresponding author. Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China.In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.http://www.sciencedirect.com/science/article/pii/S2452199X2400505X3D bioprintingArtificial intelligenceMachine learningQuality by designRegenerative medicineClinical translation |
| spellingShingle | Zhenrui Zhang Xianhao Zhou Yongcong Fang Zhuo Xiong Ting Zhang AI-driven 3D bioprinting for regenerative medicine: From bench to bedside Bioactive Materials 3D bioprinting Artificial intelligence Machine learning Quality by design Regenerative medicine Clinical translation |
| title | AI-driven 3D bioprinting for regenerative medicine: From bench to bedside |
| title_full | AI-driven 3D bioprinting for regenerative medicine: From bench to bedside |
| title_fullStr | AI-driven 3D bioprinting for regenerative medicine: From bench to bedside |
| title_full_unstemmed | AI-driven 3D bioprinting for regenerative medicine: From bench to bedside |
| title_short | AI-driven 3D bioprinting for regenerative medicine: From bench to bedside |
| title_sort | ai driven 3d bioprinting for regenerative medicine from bench to bedside |
| topic | 3D bioprinting Artificial intelligence Machine learning Quality by design Regenerative medicine Clinical translation |
| url | http://www.sciencedirect.com/science/article/pii/S2452199X2400505X |
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