AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025230052
Submit to AIH
Cite this article
13
Download
119
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
REVIEW ARTICLE

Advances in three-dimensional bioprinting and artificial intelligence for enhanced tumor modeling: Current progress and future perspectives

Jia Weng1,2 ,  Bincan Deng3 ,  Xia Huang4 ,  Xuan Xue1,2*
Show Less
1 Department of Chemistry and Materials Science, School of Science, Xi’an Jiaotong–Liverpool University, Suzhou, Jiangsu, China
2 Department of Chemistry, School of Physical Sciences, University of Liverpool, Liverpool, United Kingdom
3 Department of Foundational Mathematics, School of Mathematics and Physics, Xi’an Jiaotong–Liverpool University, Suzhou, Jiangsu, China
4 Department of Biological Sciences and Bioinformatics, School of Science, Xi’an Jiaotong–Liverpool University, Suzhou, Jiangsu, China
AIH, 025230052 https://doi.org/10.36922/AIH025230052
Received: 5 June 2025 | Revised: 18 July 2025 | Accepted: 29 July 2025 | Published online: 11 August 2025
© 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/ )
Download PDF
Cite
XML
Abstract

Over the past decade, the global increase in cancer prevalence and cancer-related mortality has fueled extensive research to enhance the effectiveness of cancer treatments. Such efforts include the fabrication of lab-grown tissues and organs for transplantation, and the development of in vitro models for cancer drug testing and screening. Notably, three-dimensional (3D) tissue models offer advantages over two-dimensional cultures and have benefited from recent advancements in cutting-edge techniques like 3D printing, enabling the reconstruction of various tumor models in vitro. In this review, we focus on recent progress in in vitro 3D tumor models, with particular emphasis on the roles of 3D bioprinting and artificial intelligence. Furthermore, we provide future perspectives on employing bioprinting to develop tumor models that accurately mimic the complexity and heterogeneity of real tumor microenvironments.

Keywords
Tumor model
Three-dimensional bioprinting
Bioink
Artificial intelligence-powered clinical diagnostic aids
Funding
This research was supported by the Postgraduate Research Scholarship (PGRS FOS2211JM02), the Research Development Funding (RDF-22-02-002) provided by Xi’an Jiaotong-Liverpool University (Suzhou, China), the Suzhou Industrial Park High Quality Innovation Platform of Functional Molecular Materials and Devices (YZCXPT2023105), and the XJTLU Advanced Materials Research Center (AMRC).
Conflict of interest
The authors declare that they have no conflicts of interest.
References
  1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. doi: 10.3322/caac.21660

 

  1. Siegel RL, Giaquinto AN, Jemal A, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12-49. doi: 10.3322/caac.21820

 

  1. Dolgin E. Cancer drug approvals and setbacks in 2024. Nat Cancer. 2024;5(12):1756-1758. doi: 10.1038/s43018-024-00873-3

 

  1. Carvalho V, Maia I, Souza A, et al. In vitro biomodels in stenotic arteries to perform blood analogues flow visualizations and measurements: A review. Open Biomed Eng J. 2020;14(1):87-102. doi: 10.2174/1874120702014010087

 

  1. Fontoura JC, Viezzer C, dos Santos FG, et al. Comparison of 2D and 3D cell culture models for cell growth, gene expression and drug resistance. Mater Sci Eng C Mater Biol Appl. 2020;107:110264. doi: 10.1016/j.msec.2019.110264

 

  1. Jubelin C, Muñoz-Garcia J, Griscom L, et al. Three-dimensional in vitro culture models in oncology research. Cell Biosci. 2022;12(1):155. doi: 10.1186/s13578-022-00887-3

 

  1. Weng J, Li S, Weng J, et al. Bioinspired 3D hydrogel scaffold to mimic tumor microenvironment for investigating into the anoikis resistance mechanisms in colorectal cancer. Mater Today Bio. 2025;33:102061. doi: 10.1016/j.mtbio.2025.102061

 

  1. Abuwatfa WH, Pitt WG, Husseini GA. Scaffold-based 3D cell culture models in cancer research. J Biomed Sci. 2024;31(1):7. doi: 10.1186/s12929-024-00994-y

 

  1. Li W, Zhou Z, Zhou X, et al. 3D biomimetic models to reconstitute tumor microenvironment in vitro: Spheroids, organoids, and tumor‐on‐a‐chip. Adv Healthc Mater. 2023;12(18):e202202609. doi: 10.1002/adhm.202202609

 

  1. Malhão F, Macedo A, Ramos A, Rocha E. Morphometrical, morphological, and immunocytochemical characterization of a tool for cytotoxicity research: 3D cultures of breast cell lines grown in ultra-low attachment plates. Toxics. 2022;10(8):415. doi: 10.3390/toxics10080415

 

  1. Watters KM, Bajwa P, Kenny HA. Organotypic 3D models of the ovarian cancer tumor microenvironment. Cancers (Basel). 2018;10(8):265. doi: 10.3390/cancers10080265

 

  1. Sato T, Vries RG, Snippert HJ, et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature. 2009;459(7244):262-265. doi: 10.1038/nature07935

 

  1. Spence JR, Mayhew CN, Rankin SA, et al. Directed differentiation of human pluripotent stem cells into intestinal tissue in vitro. Nature. 2010;470(7332):105-109. doi: 10.1038/nature09691

 

  1. Zhang Z, Zhou X, Fang Y, Xiong Z, Zhang T. AI-driven 3D bioprinting for regenerative medicine: From bench to bedside. Bioact Mater. 2025;45:201-230.} doi: 10.1016/j.bioactmat.2024.11.021

 

  1. Bedell ML, Navara AM, Du Y, Zhang S, Mikos AG. Polymeric systems for bioprinting. Chem Rev. 2020;120(19):10744-10792. doi: 10.1021/acs.chemrev.9b00834

 

  1. Ng WL, Shkolnikov V. Jetting-based bioprinting: process, dispense physics, and applications. Bio Design Manuf. 2024;7(5):771-799. doi: 10.1007/s42242-024-00285-3

 

  1. Park JA, Lee Y, Jung S. Inkjet-based bioprinting for tissue engineering. Organoid. 2023;3:e12. doi: 10.51335/organoid.2023.3.e12

 

  1. Peng Y, Zhao H, Hu S, et al. Exploring the impact of osteoprotegerin on osteoclast and precursor fusion: Mechanisms and modulation by ATP. Differentiation. 2024;138:100789. doi: 10.1016/j.diff.2024.100789

 

  1. Zhao DK, Xu HQ, Yin J, Yang HY. Inkjet 3D bioprinting for tissue engineering and pharmaceutics. J Zhejiang Univ Sci. 2022;23(12):955-973. doi: 10.1631/2023.a2200569

 

  1. Huang X, Ng WL, Yeong WY. Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches. J Intell Manuf. 2023;35(5):2349-2364. doi: 10.1007/s10845-023-02167-4

 

  1. Huang C, Lin H, Yang J. (Xiaoming). A robust method for increasing Fc glycan high mannose level of recombinant antibodies. Biotechnol Bioeng. 2015;112(6):1200-1209. doi: 10.1002/bit.25534

 

  1. Xu K, Ko SH, Chen J. Advances in wearable and implantable bioelectronics for precision medicine. Biodes Manuf. 2024;7(4):383-387. doi: 10.1007/s42242-024-00302-5

 

  1. Zeng C, Hua S, Zhou J, et al. Oral microalgae‐based biosystem to enhance irreversible electroporation immunotherapy in hepatocellular carcinoma. Adv Sci (Weinh). 2025;12(15):e2409381. doi: 10.1002/advs.202570101

 

  1. Glass AM, Patel JS, Goodby JW, Olson DH, Geary JM. Pyroelectric detection with smectic liquid crystals. J Appl Phys. 1986;60(8):2778-2782. doi: 10.1063/1.337111

 

  1. Lee CKW, Pan Y, Yang R, Kim M, Li MG. Laser-Induced Transfer of Functional Materials. Top Curr Chem (Cham). 2023;381(4):1-18. doi: 10.1007/s41061-023-00429-6

 

  1. Liu Y, Ding Y, Yang L, Sun R, Zhang T, Yang X. Research and progress of laser cladding on engineering alloys: A review. J Manuf Process. 2021;66:341-363. doi: 10.1016/j.jmapro.2021.03.061

 

  1. Alayavalli K, Bourell D. Fabrication of modified graphite bipolar plates by indirect selective laser sintering (SLS) for direct methanol fuel cells. Rapid Prototyp J. 2010;16(4):275-278. doi: 10.1108/13552541080000464

 

  1. Smith AV. How to use SNLO nonlinear optics software to select nonlinear crystals and model their performance. In: SPIE Proceedings. Vol. 4972. SPIE; 2003. p. 50. doi: 10.1117/12.472831

 

  1. Caughman JBO, Baylor LR, Guillorn MA, Merkulov VI, Lowndes DH, Allard LF. Growth of vertically aligned carbon nanofibers by low-pressure inductively coupled plasma-enhanced chemical vapor deposition. Appl Phys Lett. 2003;83(6):1207-1209. doi: 10.1063/1.1597981

 

  1. Zhou W, Yu Y, Bai S, Hu A. Laser direct writing of waterproof sensors inside flexible substrates for wearable electronics. Opt Laser Technol. 2021;135:106694. doi: 10.1016/j.optlastec.2020.106694

 

  1. Hsieh JF, Lin PD. Application of homogenous transformation matrix to measurement of cam profiles on coordinate measuring machines. Int J Mach Tools Manuf. 2007;47(10):1593-1606. doi: 10.1016/j.ijmachtools.2006.11.001

 

  1. Blasco E, Müller J, Müller P, et al. Fabrication of conductive 3D gold‐containing microstructures via direct laser writing. Adv Mater. 2016;28(18):3592-3595. doi: 10.1002/adma.201506126

 

  1. Jo Y, Park HJ, Kim Y, et al. Form‐factor free 3D copper circuits by surface‐conformal direct printing and laser writing. Adv Funct Mater. 2020;30(45):2004659. doi: 10.1002/adfm.202004659

 

  1. Li RZ, Peng R, Kihm KD, et al. High-rate in-plane micro-supercapacitors scribed onto photo paper using in situ femtolaser-reduced graphene oxide/Au nanoparticle microelectrodes. Energy Environ Sci. 2016;9(4):1458-1467. doi: 10.1039/c5ee03637b

 

  1. Zhou X, Guo W, Zhu Y, Peng P. The laser writing of highly conductive and anti-oxidative copper structures in liquid. Nanoscale. 2020;12(2):563-571. doi: 10.1039/c9nr07248a

 

  1. Mahmood MA, Popescu AC, Mihailescu IN. Metal matrix composites synthesized by laser-melting deposition: A review. Materials (Basel). 2020;13(11):2593. doi: 10.3390/ma13112593

 

  1. MacKenzie M, Chi H, Varma M, Pal P, Kaar A, Paterson L. Femtosecond laser fabrication of silver nanostructures on glass for surface enhanced Raman spectroscopy. Sci Rep. 2019;9(1):17058. doi: 10.1038/s41598-019-53328-6

 

  1. Armon N, Greenberg E, Edri E, Nagler‐Avramovitz O, Elias Y, Shpaisman H. Laser‐based printing: From liquids to microstructures. Adv Funct Mater. 2021;31(13):2008547. doi: 10.1002/adfm.202008547

 

  1. Zhou W, Bai S, Ma Y, et al. Laser-direct writing of silver metal electrodes on transparent flexible substrates with high-bonding strength. ACS Appl Mater Interfaces. 2016;8(37):24887-24892. doi: 10.1021/acsami.6b07696

 

  1. Pinkerton AJ. Laser direct metal deposition: Theory and applications in manufacturing and maintenance. In: Advances in Laser Materials Processing. Netherlands: Elsevier; 2010. p. 461-491. doi: 10.1533/9781845699819.6.461

 

  1. Lu WE, Zhang YL, Zheng ML, et al. Femtosecond direct laser writing of gold nanostructures by ionic liquid assisted multiphoton photoreduction. Opt Express. 2013;3(10):1660. doi: 10.1364/ome.3.001660

 

  1. Gąsiorowski L, Chai C, Rozanski A, et al. Regeneration in the absence of canonical neoblasts in an early branching flatworm. Nat Commun. 2025;16(1):1232. doi: 10.1038/s41467-024-54716-x

 

  1. Odde DJ, Renn MJ. Laser-guided direct writing of living cells. Biotechnol Bioeng. 2000;67(3):312-318. doi: 10.1038/s41467-024-54716-x

 

  1. Schiele NR, Corr DT, Huang Y, Raof NA, Xie Y, Chrisey DB. Laser-based direct-write techniques for cell printing. Biofabrication. 2010;2(3):032001. doi: 10.1088/1758-5082/2/3/032001

 

  1. Daly AC, Prendergast ME, Hughes AJ, Burdick JA. Bioprinting for the biologist. Cell. 2021;184(1):18-32. doi: 10.1016/j.cell.2020.12.002

 

  1. Sun W, Starly B, Daly AC, et al. The bioprinting roadmap. Biofabrication. 2020;12(2):022002. doi: 10.1088/1758-5090/ab5158

 

  1. Zein I, Hutmacher DW, Tan KC, Teoh SH. Fused deposition modeling of novel scaffold architectures for tissue engineering applications. Biomaterials. 2002;23(4):1169-1185. doi: 10.1016/s0142-9612(01)00232-0

 

  1. Yan B, Ouyang Q, Zhao Z, et al. Potent killing of HBV-related hepatocellular carcinoma by a chimeric protein of anti-HBsAg single-chain antibody and truncated Bid. Biomaterials. 2013;34(20):4880-4889. doi: 10.1016/j.biomaterials.2013.03.046

 

  1. Delaneau O, Marchini J, 1000 Genomes Project Consortium. Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel. Nat Commun. 2014;5(1):3934. doi: 10.1038/ncomms4934

 

  1. Markstedt K, Mantas A, Tournier I, Martínez Ávila H, Hägg D, Gatenholm P. 3D bioprinting human chondrocytes with nanocellulose-alginate bioink for cartilage tissue engineering applications. Biomacromolecules. 2015;16(5):1489-1496. doi: 10.1021/acs.biomac.5b00188

 

  1. Kalluru P, Vankayala R, Chiang CS, Hwang KC. Nano-graphene oxide-mediated in vivo fluorescence imaging and bimodal photodynamic and photothermal destruction of tumors. Biomaterials. 2016;95:1-10. doi: 10.1016/j.biomaterials.2016.04.006

 

  1. Kolesky DB, Truby RL, Gladman AS, Busbee TA, Homan KA, Lewis JA. 3D bioprinting of vascularized, heterogeneous cell‐laden tissue constructs. Adv Mater. 2014;26(19):3124-3130. doi: 10.1002/adma.201305506

 

  1. Kirillova A, Maxson R, Stoychev G, Gomillion CT, Ionov L. 4D biofabrication using shape‐morphing hydrogels. Adv Mater. 2017;29(46):1703443. doi: 10.1002/adma.201703443

 

  1. Marques CF, Diogo GS, Pina S, Oliveira JM, Silva TH, Reis RL. Collagen-based bioinks for hard tissue engineering applications: A comprehensive review. J Mater Sci Mater Med. 2019;30(3):32. doi: 10.1007/s10856-019-6234-x

 

  1. Nawroth JC, Barrile R, Conegliano D, van Riet S, Hiemstra PS, Villenave R. Stem cell-based lung-on-chips: The best of both worlds? Adv Drug Deliv Rev. 2019;140:12-32. doi: 10.1016/j.addr.2018.07.005

 

  1. Ng WL, Shkolnikov V. Optimizing cell deposition for inkjet-based bioprinting. Int J Bioprint. 2024;10(2):2135. doi: 10.36922/ijb.2135

 

  1. Ligon SC, Liska R, Stampfl J, Gurr M, Mülhaupt R. Polymers for 3D printing and customized additive manufacturing. Chem Rev. 2017;117(15):10212-10290. doi: 10.1021/acs.chemrev.7b00074

 

  1. AbouelNour Y, Gupta N. In-situ monitoring of sub-surface and internal defects in additive manufacturing: A review. Mater Des. 2022;222:111063. doi: 10.1016/j.matdes.2022.111063

 

  1. Tomono D, Nishimura T. Near-infrared two-wavelength shift-and-add. In: SPIE Proceedings. Vol. 3019. SPIE; 1997. p. 9. doi: 10.1117/12.275176

 

  1. Han D, Yang C, Fang NX, Lee H. Rapid multi-material 3D printing with projection micro-stereolithography using dynamic fluidic control. Additive Manufacturing. 2019;27:606-615. doi: 10.1016/j.addma.2019.03.031

 

  1. Das S, Beaman JJ, Wohlert M, Bourell DL. Freeform fabrication of high performance titanium components via SLS/HIP. MRS Proceedings. 1998;542:45-50. doi: 10.1557/proc-542-45

 

  1. Stampfl J, Baudis S, Heller C, et al. Photopolymers with tunable mechanical properties processed by laser-based high-resolution stereolithography. J Micromech Microeng. 2008;18(12):125014. doi: 10.1088/0960-1317/18/12/125014

 

  1. Lim KS, Levato R, Costa PF, et al. Bio-resin for high resolution lithography-based biofabrication of complex cell-laden constructs. Biofabrication. 2018;10(3):034101. doi: 10.1088/1758-5090/aac00c

 

  1. Kafle A, Luis E, Silwal R, Pan HM, Shrestha PL, Bastola AK. 3D/4D printing of polymers: Fused deposition modelling (FDM), selective laser sintering (SLS), and stereolithography (SLA). Polymers (Basel). 2021;13(18):3101. doi: 10.3390/polym13183101

 

  1. Jin G, Shin SH, Shim JS, Lee KW, Kim JE. Accuracy of 3D printed models and implant-analog positions according to the implant-analog-holder offset, inner structure, and printing layer thickness: An in-vitro study. Int J Dent. 2022;125:104268. doi: 10.1016/j.jdent.2022.104268

 

  1. Kumar AS, Venkatesalu S, Dilliyappan S, et al. Microfluidics as diagnostic tools. Clin Chim Acta. 2024;556:117841. doi: 10.1016/j.cca.2024.117841

 

  1. Skoog SA, Goering PL, Narayan RJ. Stereolithography in tissue engineering. J Mater Sci Mater Med. 2013;25(3):845-856. doi: 10.1007/s10856-013-5107-y

 

  1. Mao S, Pang Y, Liu T, et al. Bioprinting of in vitro tumor models for personalized cancer treatment: A review. Biofabrication. 2020;12(4):042001. doi: 10.1088/1758-5090/ab97c0

 

  1. Peng X, Xia X, Xu X, et al. Ultrafast self-gelling powder mediates robust wet adhesion to promote healing of gastrointestinal perforations. Sci Adv. 2021;7(23):eabe8739. doi: 10.1126/sciadv.abe8739

 

  1. Chen J, Sun J, Wang Q, et al. Systemic deficiency of PTEN accelerates breast cancer growth and metastasis. Front Oncol. 2022;12:825484. doi: 10.3389/fonc.2022.825484

 

  1. Cui G, Dong K, Zhou JY, et al. Spatiotemporal transcriptomic atlas reveals the dynamic characteristics and key regulators of planarian regeneration. Nat Commun. 2023;14(1):3205. doi: 10.1038/s41467-023-39016-0

 

  1. Mironov V. Printing technology to produce living tissue. Expert Opin Biol Ther. 2003;3(5):701-704. doi: 10.1517/14712598.3.5.701

 

  1. Lee VK, Dai G. Printing of three-dimensional tissue analogs for regenerative medicine. Ann Biomed Eng. 2016;45(1):115-131. doi: 10.1007/s10439-016-1613-7

 

  1. Mandrycky C, Wang Z, Kim K, Kim DH. 3D bioprinting for engineering complex tissues. Biotechnol Adv. 2016;34(4):422-434. doi: 10.1016/j.biotechadv.2015.12.011

 

  1. Hospodiuk M, Dey M, Sosnoski D, Ozbolat IT. The bioink: A comprehensive review on bioprintable materials. Biotechnol Adv. 2017;35(2):217-239. doi: 10.1016/j.biotechadv.2016.12.006

 

  1. Ferreira AM, Gentile P, Chiono V, Ciardelli G. Collagen for bone tissue regeneration. Acta Biomater. 2012;8(9):3191-3200. doi: 10.1016/j.actbio.2012.06.014

 

  1. Gómez-Guillén MC, Giménez B, López-Caballero ME, Montero MP. Functional and bioactive properties of collagen and gelatin from alternative sources: A review. Food Hydrocolloids. 2011;25(8):1813-1827. doi: 10.1016/j.foodhyd.2011.02.007

 

  1. Sun TL, Kurokawa T, Kuroda S, et al. Physical hydrogels composed of polyampholytes demonstrate high toughness and viscoelasticity. Nat Mater. 2013;12(10):932-937. doi: 10.1038/nmat3713

 

  1. Slaughter BV, Khurshid SS, Fisher OZ, Khademhosseini A, Peppas NA. Hydrogels in regenerative medicine. Adv Mater. 2009;21(32-33):3307-3329. doi: 10.1002/adma.200802106

 

  1. Seliktar D. Designing cell-compatible hydrogels for biomedical applications. Science. 2012;336(6085):1124-1128. doi: 10.1126/science.1214804

 

  1. Benton JA, DeForest CA, Vivekanandan V, Anseth KS. Photocrosslinking of gelatin macromers to synthesize porous hydrogels that promote valvular interstitial cell function. Tissue Eng Part A. 2009;15(11):3221-3230. doi: 10.1089/ten.tea.2008.0545

 

  1. Nichol JW, Koshy ST, Bae H, Hwang CM, Yamanlar S, Khademhosseini A. Cell-laden microengineered gelatin methacrylate hydrogels. Biomaterials. 2010;31(21):5536-5544. doi: 10.1016/j.biomaterials.2010.03.064

 

  1. Akimoto AM, Hasuike E, Tada H, et al. Design of tetra-arm PEG-crosslinked thermoresponsive hydrogel for 3D cell culture. Anal Sci. 2016;32(11):1203-1205. doi: 10.2116/analsci.32.1203

 

  1. Wolf MT, Daly KA, Brennan-Pierce EP, et al. A hydrogel derived from decellularized dermal extracellular matrix. Biomaterials. 2012;33(29):7028-7038. doi: 10.1016/j.biomaterials.2012.06.051

 

  1. Samandari M, Quint J, Rodríguez‐delaRosa A, Sinha I, Pourquié O, Tamayol A. Bioinks and bioprinting strategies for skeletal muscle tissue engineering. Adv Mater. 2022;34(12):e2105883. doi: 10.1002/adma.202105883

 

  1. Lasaosa FL, Zhou Y, Song J, et al. Nature-inspired scarless healing: guiding biomaterials design for advanced therapies. Tissue Eng Part B Rev. 2024;30(3):371-384. doi: 10.1089/ten.teb.2023.0224

 

  1. Noor N, Shapira A, Edri R, Gal I, Wertheim L, Dvir T. 3D printing of personalized thick and perfusable cardiac patches and hearts. Adv Sci (Weinh). 2019;6(11):1900344. doi: 10.1002/advs.201900344

 

  1. Chen H, Cheng Y, Wang X, et al. 3D printed in vitro tumor tissue model of colorectal cancer. Theranostics. 2020;10(26):12127-12143. doi: 10.7150/thno.52450

 

  1. Sbirkov Y, Molander D, Milet C, et al. A colorectal cancer 3D bioprinting workflow as a platform for disease modeling and chemotherapeutic screening. Front Bioeng Biotechnol. 2021;9:755563. doi: 10.3389/fbioe.2021.755563

 

  1. Chen X, Yue Z, Winberg PC, Lou YR, Beirne S, Wallace GG. 3D bioprinting dermal-like structures using species-specific ulvan. Biomater Sci. 2021;9(7):2424-2438. doi: 10.1039/d0bm01784a

 

  1. Derakhshanfar S, Mbeleck R, Xu K, Zhang X, Zhong W, Xing M. 3D bioprinting for biomedical devices and tissue engineering: A review of recent trends and advances. Bioact Mater. 2018;3(2):144-156. doi: 10.1016/j.bioactmat.2017.11.008

 

  1. Jin Z, Zhang Z, Shao X, Gu GX. Monitoring anomalies in 3D bioprinting with deep neural networks. ACS Biomater Sci Eng. 2023;9(7):3945-3952. doi: 10.1021/acsbiomaterials.0c01761

 

  1. Zhu Z, Ng DWH, Park HS, McAlpine MC. 3D-printed multifunctional materials enabled by artificial-intelligence-assisted fabrication technologies. Nat Rev Mater. 2020;6(1):27-47. doi: 10.1038/s41578-020-00235-2

 

  1. Xu P, Ji X, Li M, Lu W. Small data machine learning in materials science. NPJ Comput Mater. 2023;9(1):24. doi: 10.1038/s41524-023-01000-z

 

  1. Freeman S, Calabro S, Williams R, Jin S, Ye K. Bioink formulation and machine learning-empowered bioprinting optimization. Front Bioeng Biotechnol. 2022;10:913579. doi: 10.3389/fbioe.2022.913579

 

  1. Bonatti AF, Vozzi G, Chua CK, Maria CA. Deep learning quality control loop of the extrusion-based bioprinting process. Int J Bioprint. 2022;8(4):620. doi: 10.18063/ijb.v8i4.620

 

  1. Theus AS, Ning L, Hwang B, et al. Bioprintability: Physiomechanical and biological requirements of materials for 3D bioprinting processes. Polymers (Basel). 2020;12(10):2262. doi: 10.3390/polym12102262

 

  1. Vahed R, Zareie Rajani HR, Milani AS. Can a black-box AI replace costly DMA testing?-A case study on prediction and optimization of dynamic mechanical properties of 3D printed acrylonitrile butadiene styrene. Materials (Basel). 2022;15(8):2855. doi: 10.3390/ma15082855

 

  1. Deng B, Chen S, Lasaosa FL, et al. Predicting rheological properties of HAMA/GelMA hybrid hydrogels via machine learning. J Mech Behav Biomed Mater. 2025;168:107005. doi: 10.1016/j.jmbbm.2025.107005

 

  1. Chen H, Liu Y, Balabani S, Hirayama R, Huang J. Machine learning in predicting printable biomaterial formulations for direct ink writing. Research (Wash D C). 2023;6:0197. doi: 10.34133/research.0197

 

  1. Hashemi A, Ezati M, Zumberg I, et al. Characterization and optimization of a biomaterial ink aided by machine learning-assisted parameter suggestion. Mater Today Commun. 2024;40:109777. doi: 10.1016/j.mtcomm.2024.109777

 

  1. Ruberu K, Senadeera M, Rana S, et al. Coupling machine learning with 3D bioprinting to fast track optimisation of extrusion printing. Appl Mater Today. 2021;22:100914. doi: 10.1016/j.apmt.2020.100914

 

  1. Schwab A, Levato R, D’Este M, Piluso S, Eglin D, Malda J. Printability and shape fidelity of bioinks in 3D bioprinting. Chem Rev. 2020;120(19):11028-11055. doi: 10.1021/acs.chemrev.0c00084

 

  1. Lee J, Oh SJ, An SH, Kim WD, Kim SH. Machine learning-based design strategy for 3D printable bioink: Elastic modulus and yield stress determine printability. Biofabrication. 2020;12(3):035018. doi: 10.1088/1758-5090/ab8707

 

  1. Fastowicz J, Okarma K. Fast quality assessment of 3D printed surfaces based on structural similarity of image regions. In: Conference: 2018 International Interdisciplinary PhD Workshop (IIPhDW). IEEE; 2018. doi: 10.1109/iiphdw.2018.8388399

 

  1. Paraskevoudis K, Karayannis P, Koumoulos EP. Real-time 3D printing remote defect detection (stringing) with computer vision and artificial intelligence. Processes. 2020;8(11):1464. doi: 10.3390/pr8111464

 

  1. Brion DAJ, Pattinson SW. Generalisable 3D printing error detection and correction via multi-head neural networks. Nat Commun. 2022;13(1):4654. doi: 10.1038/s41467-022-31985-y

 

  1. Momoli C, Costa B, Lenti L, et al. The evolution of anticancer 3D in vitro models: The potential role of machine learning and AI in the next generation of animal-free experiments. Cancers (Basel). 2025;17(4):700. doi: 10.3390/cancers17040700

 

  1. Chen Z, Ma N, Sun X, et al. Automated evaluation of tumor spheroid behavior in 3D culture using deep learning-based recognition. Biomaterials. 2021;272:120770. doi: 10.1016/j.biomaterials.2021.120770

 

  1. Mali AK, Murugappan S, Prasad JR, Tofail SAM, Thorat ND. A deep learning pipeline for morphological and viability assessment of 3D cancer cell spheroids. Biol Methods Protoc. 2025;10(1):bpaf030. doi: 10.1093/biomethods/bpaf030

 

  1. Kang HW, Lee SJ, Ko IK, Kengla C, Yoo JJ, Atala A. A 3D bioprinting system to produce human-scale tissue constructs with structural integrity. Nat Biotechnol. 2016;34(3):312-319. doi: 10.1038/nbt.3413

 

  1. Kolesky DB, Homan KA, Skylar-Scott MA, Lewis JA. Three-dimensional bioprinting of thick vascularized tissues. Proc Natl Acad Sci U S A. 2016;113(12):3179-3184. doi: 10.1073/pnas.1521342113

 

  1. Lee A, Hudson AR, Shiwarski DJ, et al. 3D bioprinting of collagen to rebuild components of the human heart. Science. 2019;365(6452):482-487. doi: 10.1126/science.aav9051

 

  1. Zhang YS, Arneri A, Bersini S, Shin SR, et al. Bioprinting 3D microfibrous scaffolds for engineering endothelialized myocardium and heart-on-a-chip. Biomaterials. 2016;110:45-59. doi: 10.1016/j.biomaterials.2016.09.003

 

  1. Hinton TJ, Jallerat Q, Palchesko RN, et al. Three-dimensional printing of complex biological structures by freeform reversible embedding of suspended hydrogels. Sci Adv. 2015;1(9):e1500758. doi: 10.1126/sciadv.1500758

 

  1. Zhang YS, Yue K, Aleman J, et al. 3D bioprinting for tissue and organ fabrication. Ann Biomed Eng. 2017;45(1):148-163. doi: 10.1007/s10439-016-1612-8

 

  1. Chae S, Ha DH, Lee H. 3D bioprinting strategy for engineering vascularized tissue models. Int J Bioprint. 2023;9(5):748. doi: 10.18063/ijb.748

 

  1. Badylak SF, Weiss DJ, Caplan A, Macchiarini P. Engineered whole organs and complex tissues. Lancet. 2012;379(9819):943-952. doi: 10.1016/S0140-6736(12)60073-7

 

  1. Langer R, Vacanti JP. Tissue engineering. Science. 1993;260(5110):920-926. doi: 10.1126/science.8493529

 

  1. Kirillova A, Bushev S, Abubakirov A, Sukikh G. Bioethical and legal issues in 3D bioprinting. Int J Bioprint. 2020;6(3):272. doi: 10.18063/ijb.v6i3.272

 

  1. Datta P, Cabrera LY, Ozbolat IT. Ethical challenges with 3D bioprinted tissues and organs. Trends Biotechnol. 2023;41(1):6-9. doi: 10.1016/j.tibtech.2022.08.012

 

  1. Jian H, Wang M, Wang S, Wang A, Bai S. 3D bioprinting for cell culture and tissue fabrication. Biodes Manuf. 2018;1(1):45-61. doi: 10.1007/s42242-018-0006-1

 

  1. Groll J, Boland T, Blunk T, et al. Biofabrication: Reappraising the definition of an evolving field. Biofabrication. 2016;8(1):013001. doi: 10.1088/1758-5090/8/1/013001

 

  1. Williams DJ, Sebastine IM. Tissue engineering and regenerative medicine: Manufacturing challenges. IEE Proc Nanobiotechnol. 2005;152(6):207-210. doi: 10.1049/ip-nbt:20050001

 

  1. Moroni L, Boland T, Burdick JA, et al. Biofabrication: A guide to technology and terminology. Trends Biotechnol. 2018;36(4):384-402. doi: 10.1016/j.tibtech.2017.10.015

 

  1. Ozbolat IT, Moncal KK, Gudapati H. Evaluation of bioprinter technologies. Addit Manuf. 2017;13:179-200. doi: 10.1016/j.addma.2016.10.003

 

  1. Papadimitroulas P, Brocki L, Christopher Chung N, et al. Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization. Phys Med. 2021;83:108-121. doi: 10.1016/j.ejmp.2021.03.009

 

  1. An J, Chua CK, Mironov V. Application of machine learning in 3D bioprinting: Focus on development of big data and digital twin. Int J Bioprint. 2021;7(1):342. doi: 10.18063/ijb.v7i1.342

 

  1. Rodríguez-Salvador M, Rio-Belver RM, Garechana- Anacabe G. Scientometric and patentometric analyses to determine the knowledge landscape in innovative technologies: The case of 3D bioprinting. PLoS One. 2017;12(6):e0180375. doi: 10.1371/journal.pone.0180375

 

  1. García-García LA, Rodríguez-Salvador M. Disclosing main authors and organisations collaborations in bioprinting through network maps analysis. J Biomed Semantics. 2020;11(1):3. doi: 10.1186/s13326-020-0219-z
Share
Back to top
Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept