AccScience Publishing / IJB / Volume 12 / Issue 5 / DOI: 10.36922/IJB026320337
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REVIEW ARTICLE

Intelligent bioprinting: Smart bioinks, closed-loop biofabrication, and precision therapeutics

Fanfan Zhou1,2,3† ,  Xingfei Li1† ,  Jingtian Chen4 ,  Wanxiang Yao3 ,  Yuzhi Lin3 ,  Zhaoyu Chen1,2 ,  Hu Li1,2 ,  Haiyan Yao3* ,  Aiming Zhang3* ,  Zhen Yang1,2*
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1 Peking University People’s Hospital, Beijing , China
2 Arthritis Clinical and Research Center, Peking University People’s Hospital, Beijing , China
3 Department of Spine Surgery, Zhongshan People’s Hospital, Zhongshan, Guangdong , China
4 Department of Biomedical Engineering, Sichuan University, Chengdu, Sichuan , China
†These authors contributed equally to this work.
IJB 2026, 12(5), 026320337 https://doi.org/10.36922/IJB026320337
Received: 4 August 2026 | Revised: 5 September 2026 | Accepted: 9 September 2026 | Published online: 9 September 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Three-dimensional bioprinting is moving beyond anatomical fabrication toward systems that can respond to biological cues, use process data to guide manufacturing, and support patient-specific applications. Rather than treating every responsive material, artificial-intelligence model, or sensor-equipped platform as intelligent, this review uses four descriptive functions—responsiveness, observability, decision capability, and actuation—to compare the field across three operational categories: responsive components, intelligence-assisted systems, and validated closed-loop systems. These functions are not cumulative prerequisites: responsiveness can exist without sensing, and closed-loop control can operate without a responsive bioink. We synthesize smart bioinks and printing quality, intelligent strategy selection, data-driven monitoring and feedback, post-printing maturation, and regenerative or precision-therapeutic applications. Representative evidence indicates that the most credible closed loops currently address fast manufacturing variables such as extrusion and geometry, whereas biological feedback over hours to weeks, continuously updated digital twins, and high-level autonomy remain early. We also distinguish the evidence needs of implantable regenerative constructs from those of patient-derived drug-testing models. The contribution of this review is therefore a cross-domain functional classification and translational evidence hierarchy, rather than a claim that smart materials, AI-assisted design, or closed-loop bioprinting are themselves new.

Graphical abstract
Keywords
Intelligent bioprinting
Smart bioinks
Closed-loop biofabrication
Biosensing
Regenerative medicine
Disease models
Precision therapeutics
Funding
This work was financially supported by the Peking University Medicine Fund of Fostering Young Scholars’ Scientific & Technological Innovation (BMU2026YFJHPY022), Peking University People’s Hospital Scientific Research Development Funds (RDEB2025-04), and Bone and Joint Science and Technology Talent Innovation Development Project (GSKQNJJ-2025-001).
Conflict of interest
The authors declare they have no competing interests.
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing
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