AccScience Publishing / IJB / Volume 12 / Issue 1 / DOI: 10.36922/IJB025390404
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REVIEW ARTICLE

AI-driven integration of multimodal CT/MRI imaging and 3D printing in medicine: Advances, clinical applications, and future directions

Weipeng Zhou1 ,  Lihua Gao1 ,  Meng Wu1 ,  Jianhua Liu1*
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1 Department of Radiology, The Second Hospital of Jilin University, Changchun, Jilin, China
IJB 2026, 12(1), 125–144; https://doi.org/10.36922/IJB025390404
Received: 28 September 2025 | Accepted: 3 November 2025 | Published online: 7 November 2025
(This article belongs to the Special Issue 3D-Printed Biomedical Devices)
© 2025 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

The integration of artificial intelligence (AI)-enabled multimodal computed tomography (CT)/magnetic resonance imaging (MRI) medical imaging and three-dimensional (3D) printing technology represents a pivotal direction in medical engineering for advancing precision diagnosis and therapy. Multimodal data fusion serves as the primary strategy to enhance the accuracy of 3D-printed models; however, cross-modal data fusion is hindered by inherent technical challenges, including failures in feature alignment and discrepancies in the physical properties of imaging datasets. In recent years, the advancement and seamless integration of AI technology have emerged as the core link bridging the entire workflow, from multimodal CT/MRI imaging acquisition to 3D printing, offering novel paradigms for the development of high-precision 3D printing technology in clinical settings. This review systematically elaborates on AI’s core technical underpinnings for multimodal imaging and 3D printing: AI effectively mitigates integration and adaptation hurdles arising from intrinsic discrepancies in data source characteristics through three key pathways—artifact reduction and optimization of raw imaging data, precise cross-modal registration, and fine-grained segmentation of anatomical structures. Furthermore, AI-driven optimization of 3D rendering effects, combined with four-view projection, significantly enhances the fidelity of anatomical detail reproduction, thereby minimizing the matching error between 3D-printed models and in vivo physical entities. Subsequently, the review details the clinical application value of multimodal 3D printing technology across key medical specialties, including orthopedics, oncological surgery, dentistry, and vascular surgery, while concomitantly highlighting prevailing challenges in technical translation and clinical adoption. Finally, it outlines future development directions from three critical dimensions: technological synergy (among AI, imaging, and 3D printing), material advancement (targeting durability and functional adaptability), and application expansion (to underserved clinical scenarios such as rehabilitation). This work aims to provide a comprehensive reference for accelerating the clinical translation of this interdisciplinary technology.  

Graphical abstract
Keywords
3D printing technology
AI-enabled image preprocessing
Cross-modal data alignment
Multimodal CT/MRI imaging
Funding
This work was supported by the Innovation and Entrepreneurship Talent Funding Program of Jilin Province,china and the Health Special Project of the Finance Department of Jilin Province, China.
Conflict of interest
The authors declare that 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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