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REVIEW ARTICLE

Bioprinting and artificial intelligence strategies for additively manufactured wearable biosensors

Zhuoyuan Yang1† ,  Zefu Ren1† ,  Yuxuan Wu1† ,  Meng Cheng2,3,4* ,  Jia An5 ,  Sirish Namilae1* ,  Chee Kai Chua2,3,4* ,  Yizhou Jiang1*
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1 Department of Aerospace Engineering, Embry-Riddle Aeronautical University, Daytona Beach, Florida 32114 , United States
2 Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei 430081 , China
3 Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, Hubei 430081 , China
4 Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081 , China
5 Engineering Product Development Pillar, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372 , Singapore
†These authors contributed equally to this work.
IJB, 026260276 https://doi.org/10.36922/IJB026260276
Received: 28 June 2026 | Revised: 30 August 2026 | Accepted: 3 September 2026 | Published online: 7 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

Additively manufactured wearable biosensors enable continuous, noninvasive, and real-time monitoring of physiological and biochemical signals during daily use. Recent advances in artificial intelligence (AI) provide complementary tools for identifying printable material windows, optimizing formulations and printing parameters, monitoring fabrication quality, and interpreting noisy, high-dimensional sensing data. These capabilities are particularly relevant to wearable biosensors, whose performance depends on manufacturing reproducibility and reliable signal analysis during continuous or multimodal operation. This review summarizes recent progress in the AI-assisted development of additively manufactured wearable biosensors across three stages: pre-printing design, in-process monitoring and control, and post-printing signal interpretation and application. By organizing the field around this workflow, we clarify how AI supports material selection, fabrication optimization, device evaluation, and wearable sensing, and identify the remaining challenges in developing more reliable and adaptive biosensing systems.

Graphical abstract
Keywords
Artificial intelligence
Additive manufacturing
Wearable biosensors
Biomaterials
Bioprinting
Biosignals
Funding
The work was supported by the National Natural Science Foundation of China under grant no. 52201272.
Conflict of interest
Prof. Chee Kai Chua and Dr. Jia An serve as the Editor-in-Chief and the Assistant Editor of this journal, respectively, but they were not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. 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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