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REVIEW ARTICLE

Advancing sustainability in bioprinting through artificial intelligence

Hongyi Chen1,2* ,  Jie Huang2
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1 Department of Computer Science, Faculty of Engineering Sciences, University College London, London, United Kingdom
2 Department of Mechanical Engineering, Faculty of Engineering Sciences, University College London, London, United Kingdom
IJB, 025170164 https://doi.org/10.36922/IJB025170164
Received: 24 April 2025 | Accepted: 1 July 2025 | Published online: 10 July 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/ )
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Abstract

Sustainable bioprinting is a transformative approach in tissue engineering and regenerative medicine, offering solutions to environmental challenges while advancing functional outcomes. However, achieving true sustainability remains complex, requiring reductions in material waste and energy use, and scalable, resource-efficient fabrication without compromising biological performance. Artificial intelligence (AI) provides a powerful means to meet these demands through data-driven material design, predictive process optimization, and intelligent control systems that improve both efficiency and environmental impact across the bioprinting workflow. This review examines the integration of AI into sustainable bioprinting across four key areas: hydrogel material discovery and development, bioink screening, process parameter optimization, and AI-assisted intelligent printing. AI facilitates the design of eco-friendly hydrogels by predicting molecular interactions and tailoring structural properties. It also improves bioink formulation by optimizing printability, biocompatibility, and mechanical strength, thereby reducing reliance on resource-intensive trial-and-error experimentation. Furthermore, AI algorithms streamline workflows by dynamically adjusting printing parameters to improve fidelity and reduce waste, while advanced AI-assisted systems demonstrate the feasibility of multi-material, contactless bioprinting, aligning with sustainability goals.  

Graphical abstract
Keywords
Artificial intelligence
Biomaterials
Bioprinting
Sustainability
Funding
None.
Conflict of interest
The authors declare no conflicts of interest.
References
  1. Murphy SV, De Coppi P, Atala A. Opportunities and challenges of translational 3D bioprinting. Nat Biomed Eng. 2020;4(4):370-380. doi: 10.1038/s41551-019-0471-7
  2. Mota C, Camarero-Espinosa S, Baker MB, Wieringa P, Moroni L. Bioprinting: from tissue and organ development to in vitro models. Chem Rev. 2020;120(19):10547-10607. doi: 10.1021/acs.chemrev.9b00789
  3. Kim JJ, Cho D-W. Advanced strategies in 3D bioprinting for vascular tissue engineering and disease modelling using smart bioinks. Virtual Phys Prototyp. 2024;19(1):e2395470. doi: 10.1080/17452759.2024.2395470
  4. Narayan R, Yoo J, Atala A. 3D bioprinting: physical and chemical processes. Appl Phys Rev. 2021;8(3). doi: 10.1063/5.0060283
  5. Yilmaz B, Al Rashid A, Mou YA, Evis Z, Koç M. Bioprinting: a review of processes, materials and applications. Bioprinting. 2021;23:e00148. doi: 10.1016/j.bprint.2021.e00148
  6. Sun X, Ren W, Xie L, et al. Recent advances in 3D bioprinting of tissues and organs for transplantation and drug screening. Virtual Phys Prototyp. 2024;19(1):e2384662. doi: 10.1080/17452759.2024.2384662
  7. Chua CK, An J, Fan S, et al. A perspective on transformative bioprinting. IJB. 2024;11(1):1–29. doi: 10.36922/ijb.3525
  8. Jain P, Kathuria H, Dubey N. Advances in 3D bioprinting of tissues/organs for regenerative medicine and in-vitro models. Biomaterials. 2022;287:121639. doi: 10.1016/j.biomaterials.2022.121639
  9. Faber L, Yau A, Chen Y. Translational biomaterials of four-dimensional bioprinting for tissue regeneration. Biofabrication. 2024;16(1):012001. doi: 10.1088/1758-5090/acfdd0
  10. Zhang YS, Haghiashtiani G, Hübscher T, et al. 3D extrusion bioprinting. Nat Rev Methods Primers. 2021;1(1):75. doi: 10.1038/s43586-021-00073-8
  11. Zhou J, See CW, Sreenivasamurthy S, Zhu D. Customized additive manufacturing in bone scaffolds—the gateway to precise bone defect treatment. Research. 2023;6:0239. doi: 10.34133/research.0239
  12. Senior AW, Evans R, Jumper J, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577(7792):706-710. doi: 10.1038/s41586-019-1923-7
  13. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583-589. doi: 10.1038/s41586-021-03819-2
  14. Chen H, Liu Y, Balabani S, Hirayama R, Huang J. Machine learning in predicting printable biomaterial formulations for direct ink writing. Research. 2023;6:0197. doi: 10.34133/research.0197
  15. Elbadawi M, Li H, Sun S, Alkahtani ME, Basit AW, Gaisford S. Artificial intelligence generates novel 3D printing formulations. Appl Mater Today. 2024;36:102061. doi: 10.1016/j.apmt.2024.102061
  16. 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
  17. Abramson J, Adler J, Dunger J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. 2024;630(8016):493-500. doi: 10.1038/s41586-024-07487-w
  18. Chen H, Zhang B, Huang J. Recent advances and applications of artificial intelligence in 3D bioprinting. Biophys Rev. 2024;5(3):031301. doi: 10.1063/5.0190208
  19. Thompson A. Employing artificial intelligence to augment 3D bioprinting. Scilight. 2024;2024(29):291104. doi: 10.1063/10.0028061
  20. Filippi M, Mekkattu M, Katzschmann RK. Sustainable biofabrication: from bioprinting to AI-driven predictive methods. Trends Biotechnol. 2025;43(2):290-303. doi: 10.1016/j.tibtech.2024.07.002
  21. Foyt DA, Norman MDA, Yu TTL, Gentleman E. Exploiting Advanced Hydrogel Technologies to Address Key Challenges in Regenerative Medicine. Adv Healthc Mater. 2018;7(8):1700939. doi: 10.1002/adhm.201700939
  22. Pereira RF, Bártolo PJ. 3D bioprinting of photocrosslinkable hydrogel constructs. J Appl Polym Sci. 2015; 132(48). doi: 10.1002/app.42458
  23. Hölzl K, Lin S, Tytgat L, Van Vlierberghe S, Gu L, Ovsianikov A. Bioink properties before, during and after 3D bioprinting. Biofabrication. 2016;8(3):032002. doi: 10.1088/1758-5090/8/3/032002
  24. Ng WL, Shkolnikov V. Jetting-based bioprinting: process, dispense physics, and applications. Bio Des Manuf. 2024;7(5):771-799. doi: 10.1007/s42242-024-00285-3
  25. Muthusamy S, Kannan S, Lee M, et al. 3D bioprinting and microscale organization of vascularized tissue constructs using collagen-based bioink. Biotechnol Bioeng. 2021;118(8):3150-3163. doi: 10.1002/bit.27838
  26. 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
  27. Chen H, Stampoultzis T, Papadopoulou A, Balabani S, Huang J. Evaluation of rheological properties and shape fidelity of polycaprolactone/hydroxyapatite inks for 3D printing of osteochondral tissue scaffolds. Orthop Proc. 2021;103-B(SUPP_2):96-96. doi: 10.1302/1358-992X.2021.2.096
  28. You S, Xiang Y, Hwang HH, et al. High cell density and high-resolution 3D bioprinting for fabricating vascularized tissues. Sci Adv. 2023;9(8):eade7923. doi: 10.1126/sciadv.ade7923
  29. Chen H, Khong J, Huang J. Direct ink writing of polycaprolactone/laponite composite for bone implants: 3D characterization using x-ray micro CT. Orthop Proc. 2021;103-B(SUPP_16):74-74. doi: 10.1302/1358-992X.2021.16.07
  30. Guillotin B, Souquet A, Catros S, et al. Laser assisted bioprinting of engineered tissue with high cell density and microscale organization. Biomaterials. 2010;31(28):7250-7256. doi: 10.1016/j.biomaterials.2010.05.055
  31. Keriquel V, Oliveira H, Rémy M, et al. In situ printing of mesenchymal stromal cells, by laser-assisted bioprinting, for in vivo bone regeneration applications. Sci Rep. 2017;7(1):1778. doi: 10.1038/s41598-017-01914-x
  32. Afridi A, Al Rashid A, Koç M. Recent advances in the development of stereolithography-based additive manufacturing processes: a review of applications and challenges. Bioprinting. 2024;43:e00360. doi: 10.1016/j.bprint.2024.e00360
  33. Zuev DM, Nguyen AK, Putlyaev VI, Narayan RJ. 3D printing and bioprinting using multiphoton lithography. Bioprinting. 2020;20:e00090. doi: 10.1016/j.bprint.2020.e00090
  34. Mandrycky C, Wang Z, Kim K, Kim D-H. 3D bioprinting for engineering complex tissues. Biotechnol Adv. 2016;34(4):422-434. doi: 10.1016/j.biotechadv.2015.12.011
  35. Sun W, Starly B, Daly AC, et al. The bioprinting roadmap. Biofabrication. 2020;12(2):022002. doi: 10.1088/1758-5090/ab5158
  36. Fang Y, Guo Y, Wu B, et al. Expanding embedded 3D bioprinting capability for engineering complex organs with freeform vascular networks. Adv Mater. 2023;35(22):2205082. doi: 10.1002/adma.202205082
  37. Sheybanikashani S, Zandi N, Hosseini D, Lotfi R, Simchi A. A sustainable and self-healable silk fibroin nanocomposite with antibacterial and drug eluting properties for 3D printed wound dressings. J Mater Chem B. 2024; 12(3):784-799. doi: 10.1039/D3TB02363J
  38. Qamruzzaman M, Ahmed F, Mondal MIH. An overview on starch-based sustainable hydrogels: potential applications and aspects. J Polym Environ. 2022;30(1):19-50. doi: 10.1007/s10924-021-02180-9
  39. Soman SS, Govindraj M, Hashimi NA, Zhou J, Vijayavenkataraman S. Bioprinting of human neural tissues using a sustainable marine tunicate-derived bioink for translational medicine applications. IJB. 2022;8(4):604. doi: 10.18063/ijb.v8i4.604
  40. Arif ZU, Khalid MY, Noroozi R, et al. Additive manufacturing of sustainable biomaterials for biomedical applications. Asian J Pharm Sci. 2023;18(3):100812. doi: 10.1016/j.ajps.2023.100812
  41. Whenish R, Ramakrishna S, Jaiswal AK, Manivasagam G. A framework for the sustainability implications of 3D bioprinting through nature-inspired materials and structures. Bio Des Manuf. 2022;5(2):412-423. doi: 10.1007/s42242-021-00168-x
  42. Charlet A, Hirsch M, Schreiber S, Amstad E. Recycling of load-bearing 3D printable double network granular hydrogels. Small. 2022;18(12):2107128. doi: 10.1002/smll.202107128
  43. Merotto E, Pavan PG, Piccoli M. Three-dimensional bioprinting of naturally derived hydrogels for the production of biomimetic living tissues: benefits and challenges. Biomedicines. 2023;11(6):1742. doi: 10.3390/biomedicines11061742.
  44. Guan Q-F, Yang H-B, Han Z-M, Ling Z-C, Yu S-H. An all-natural bioinspired structural material for plastic replacement. Nat Commun. 2020;11(1):5401. doi: 10.1038/s41467-020-19174-1
  45. Drury JL, Mooney DJ. Hydrogels for tissue engineering: scaffold design variables and applications. Biomaterials. 2003;24(24):4337-4351. doi: 10.1016/S0142-9612(03)00340-5
  46. Park JY, Choi Y-J, Shim J-H, Park JH, Cho D-W. Development of a 3D cell printed structure as an alternative to autologs cartilage for auricular reconstruction. J Biomed Mater Res Part B App Biomater. 2017;105(5):1016-1028. doi: 10.1002/jbm.b.33639
  47. Chen H, Gonnella G, Huang J, Di-Silvio L. Fabrication of 3D bioprinted bi-phasic scaffold for bone-cartilage interface regeneration. Biomimetics. 2023;8(1):87. doi: 10.3390/biomimetics8010087
  48. Critchley S, Kelly D. Bioinks for bioprinting functional meniscus and articular cartilage. J 3D Print Med. 2017;1:269-290. doi: 10.2217/3dp-2017-0012
  49. Unagolla JM, Jayasuriya AC. Hydrogel-based 3D bioprinting: A comprehensive review on cell-laden hydrogels, bioink formulations, and future perspectives. Appl Mater Today. 2020;18:100479. doi: 10.1016/j.apmt.2019.100479
  50. Chung JHY, Naficy S, Yue Z, et al. Bio-ink properties and printability for extrusion printing living cells. Biomater Sci. 2013;1(7):763-773. doi: 10.1039/C3BM00012E
  51. Nordahl SL, Scown CD. Recommendations for life-cycle assessment of recyclable plastics in a circular economy. Chem Sci. 2024;15(25):9397-9407. doi: 10.1039/D4SC01340A
  52. Oladapo BI, Bowoto OK, Adebiyi VA, Ikumapayi OM. Net zero on 3D printing filament recycling: a sustainable analysis. Sci Total Environ. 2023;894:165046. doi: 10.1016/j.scitotenv.2023.165046
  53. Xu X, Eatmon YL, Christie KSS, et al. Tough and recyclable phase-separated supramolecular gels via a dehydration– hydration cycle. JACS Au. 2023;3(10):2772-2779. doi: 10.1021/jacsau.3c00326
  54. Ji D, Liu J, Zhao J, et al. Sustainable 3D printing by reversible salting-out effects with aqueous salt solutions. Nat Commun. 2024;15(1):3925. doi: 10.1038/s41467-024-48121-7
  55. Sun Y, Yu K, Nie J, et al. Modeling the printability of photocuring and strength adjustable hydrogel bioink during projection-based 3D bioprinting. Biofabrication. 2021;13(3):035032. doi: 10.1088/1758-5090/aba413
  56. GhavamiNejad A, Ashammakhi N, Wu XY, Khademhosseini A. Crosslinking strategies for 3D bioprinting of polymeric hydrogels. Small. 2020;16(35):2002931. doi: 10.1002/smll.202002931
  57. Naghieh S, Chen X. Printability–a key issue in extrusion-based bioprinting. J Pharm Anal. 2021;11(5):564-579. doi: 10.1016/j.jpha.2021.02.001
  58. McCarthy J. The inversion of functions defined by Turing machines. Automata Studies. Princeton University Press, Princeton; 1956:177-181.
  59. Karthikeyan R, Geetha P, Ramaraj E. Rule based system for better prediction of diabetes. In: 2019 3rd International Conference on Computing and Communications Technologies (ICCCT). Chennai, India; 2019: 195-203. doi: 10.1109/ICCCT2.2019.8824842.
  60. Liu H, Gegov A, Cocea M. Rule-based systems: a granular computing perspective. Granul Comput. 2016;1(4):259-274. doi: 10.1007/s41066-016-0021-6
  61. Asemi A, Ko A, Nowkarizi M. Intelligent libraries: a review on expert systems, artificial intelligence, and robot. Libr Hi Tech. 2021;39:412-434. doi: 10.1108/LHT-02-2020-0038
  62. Wang L, Yan J, Mu L, Huang L. Knowledge discovery from remote sensing images: a review. WIREs Data Min Knowl Discov. 2020;10(5):e1371. doi: 10.1002/widm.1371
  63. Georgevici AI, Terblanche M. Neural networks and deep learning: a brief introduction. Intensive Care Med. 2019;45(5):712-714. doi: 10.1007/s00134-019-05537-w
  64. Shane S, Jemin G, Carl B. Scaling distributed artificial intelligence/machine learning for decision dominance in all-domain operations. In: Proceedings SPIE 12113, Artificial Intelligence and Machine Learning for Multi-domain Operations Applications IV; 2022:1211306. doi: 10.1117/12.2621199
  65. Kumari NMJ, Krishna KKV. Prognosis of diseases using machine learning algorithms: a survey. In: 2018 International Conference on Current Trends Towards Converging Technologies (ICCTCT). Coimbatore, India; 2018:1-9. doi: 10.1109/ICCTCT.2018.8550902.
  66. Chen Y, Chen H, Harker A, Liu Y, Huang J. A supervised machine learning tool to predict the bactericidal efficiency of nanostructured surface. J Nanobiotechnol. 2024;22(1):748. doi: 10.1186/s12951-024-02974-8
  67. Bonatti AF, Vozzi G, De Maria C. Enhancing quality control in bioprinting through machine learning. Biofabrication. 2024;16(2):022001. doi: 10.1088/1758-5090/ad2189
  68. Gan Z, Li H, Wolff SJ, et al. Data-driven microstructure and microhardness design in additive manufacturing using a self-organizing map. Engineering. 2019;5(4):730-735. doi: 10.1016/j.eng.2019.03.014
  69. Ramesh S, Deep A, Tamayol A, Kamaraj A, Mahajan C, Madihally S. Advancing 3D bioprinting through machine learning and artificial intelligence. Bioprinting. 2024;38:e00331. doi: 10.1016/j.bprint.2024.e00331
  70. Rathore AS, Nikita S, Thakur G, Mishra S. Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends Biotechnol. 2023;41(4):497-510. doi: 10.1016/j.tibtech.2022.08.007
  71. Ng WL, Goh GL, Goh GD, Ten JSJ, Yeong WY. Progress and opportunities for machine learning in materials and processes of additive manufacturing. Adv Mater. 2024;36(34):2310006. doi: 10.1002/adma.202310006
  72. Grira S, Mozumder MS, Mourad A-HI, Ramadan M, Khalifeh HA, Alkhedher M. 3D bioprinting of natural materials and their AI-enhanced printability: a review. Bioprinting. 2025;46:e00385. doi: 10.1016/j.bprint.2025.e00385
  73. Yu C, Jiang J. A perspective on using machine learning in 3D bioprinting. IJB. 2020;6(1):253. doi: 10.18063/ijb.v6i1.253
  74. Sun J, Yao K, An J, Jing L, Huang K, Huang D. Machine learning and 3D bioprinting. IJB. 2023;9(4):717. doi: 10.18063/ijb.717
  75. Wu Y, Ding X, Wang Y, Ouyang D. Harnessing the power of machine learning into tissue engineering: current progress and future prospects. Burns Trauma. 2024;12. doi: 10.1093/burnst/tkae053
  76. Ma L, Yu S, Xu X, Moses Amadi S, Zhang J, Wang Z. Application of artificial intelligence in 3D printing physical organ models. Mater Today Bio. 2023;23:100792. doi: 10.1016/j.mtbio.2023.100792
  77. Li Z, Song P, Li G, et al. AI energized hydrogel design, optimization and application in biomedicine. Mater Today Bio. 2024;25:101014. doi: 10.1016/j.mtbio.2024.101014
  78. Hardian R, Liang Z, Zhang X, Szekely G. Artificial intelligence: the silver bullet for sustainable materials development. Green Chem. 2020;22(21):7521-7528. doi: 10.1039/D0GC02956D
  79. Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23(1):40-55. doi: 10.1038/s41580-021-00407-0
  80. Nadernezhad A, Groll J. Machine learning reveals a general understanding of printability in formulations based on rheology additives. Adv Sci. 2022;9(29):2202638. doi: 10.1002/advs.202202638
  81. Lee J, Oh SJ, An SH, Kim W-D, Kim S-H. 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
  82. Zhang X, Chang T, Chen H, et al. Optimizing laser parameters and exploring building direction dependence of corrosion behavior in NiTi alloys fabricated by laser powder bed fusion. J Mater Res Technol. 2024;33:4023-4032. doi: 10.1016/j.jmrt.2024.10.105
  83. Ng WL, An J, Chua CK. Process, material, and regulatory considerations for 3D printed medical devices and tissue constructs. Engineering. 2024;36:146-166. doi: 10.1016/j.eng.2024.01.028
  84. Ng WL, Alvin C, Soon OY, Chua CK. Deep learning for fabrication and maturation of 3D bioprinted tissues and organs. Virtual Phys Prototyp. 2020;15(3):340-358. doi: 10.1080/17452759.2020.1771741
  85. Bone JM, Childs CM, Menon A, et al. Hierarchical machine learning for high-fidelity 3D printed biopolymers. ACS Biomater Sci Eng. 2020;6(12):7021-7031. doi: 10.1021/acsbiomaterials.0c00755
  86. Chen B, Dong J, Ruelas M, et al. Artificial intelligence-assisted high-throughput screening of printing conditions of hydrogel architectures for accelerated diabetic wound healing. Adv Funct Mater. 2022;32(38):2201843. doi: 10.1002/adfm.202201843
  87. Fu Z, Angeline V, Sun W. Evaluation of printing parameters on 3D extrusion printing of pluronic hydrogels and machine learning guided parameter recommendation. IJB. 2021;7(4):434. doi: 10.18063/ijb.v7i4.434
  88. Xu H, Liu Q, Casillas J, et al. Prediction of cell viability in dynamic optical projection stereolithography-based bioprinting using machine learning. J Intell Manuf. 2022;33(4):995-1005. doi: 10.1007/s10845-020-01708-5
  89. Zhang C, Elvitigala KCML, Mubarok W, Okano Y, Sakai S. Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting. Virtual Phys Prototyp. 2024; 19(1):e2400330. doi: 10.1080/17452759.2024.2400330
  90. Rojek I, Mikołajewski D, Kopowski J, Kotlarz P, Piechowiak M, Dostatni E. Reducing waste in 3D printing using a neural network based on an own elbow exoskeleton. Materials. 2021;14(17):5074. doi: 10.3390/ma14175074.
  91. Wu D, Xu C. Predictive modeling of droplet formation processes in inkjet-based bioprinting. J Manuf Sci Eng. 2018;140(10):101007. doi: 10.1115/1.4040619
  92. Chen H, Bansal S, Plasencia DM, et al. Omnidirectional and multi-material in situ 3D printing using acoustic levitation. Adv Mater Technol. 2025;10(9):2401792. doi: 10.1002/admt.202401792
  93. Wang X, Yang C, Yu Y, Zhao Y. In situ 3D bioprinting living photosynthetic scaffolds for autotrophic wound healing. Research. 2022;2022:9794745. doi: 10.34133/2022/9794745
  94. Zhao W, Hu C, Lin S, et al. A closed-loop minimally invasive 3D printing strategy with robust trocar identification and adaptive alignment. Addit Manuf. 2023;73:103701. doi: 10.1016/j.addma.2023.103701
  95. Montano-Murillo R, Hirayama R, Plasencia DM. OpenMPD: a low-level presentation engine for multimodal particle-based displays. ACM Trans Graph. 2023;42(2):Article 24. doi: 10.1145/3572896
  96. Hirayama R, Christopoulos G, Martinez Plasencia D, Subramanian S. High-speed acoustic holography with arbitrary scattering objects. Sci Adv. 2022;8(24):eabn7614. doi: 10.1126/sciadv.abn7614
  97. Chen H, Bansal S, Plasencia DM, et al. Omnidirectional and multi-material in situ 3D printing using acoustic levitation (Adv Mater Technol 9/2025). Adv Mater Technol. 2025;10(9):2570049. doi: 10.1002/admt.202570049
  98. Zboinska MA, Sämfors S, Gatenholm P. Robotically 3D printed architectural membranes from ambient dried cellulose nanofibril-alginate hydrogel. Mater Des. 2023;236:112472. doi: 10.1016/j.matdes.2023.112472
  99. Arora A, Alderman JE, Palmer J, et al. The value of standards for health datasets in artificial intelligence-based applications. Nat Med. 2023;29(11):2929-2938. doi: 10.1038/s41591-023-02608-w
  100. Aldoseri A, Al-Khalifa KN, Hamouda AM. Re-thinking data strategy and integration for artificial intelligence: concepts, opportunities, and challenges. Appl Sci. 2023;13(12):7082. doi: 10.3390/app13127082
  101. Shin J, Lee Y, Li Z, Hu J, Park SS, Kim K. Optimized 3D bioprinting technology based on machine learning: a review of recent trends and advances. Micromachines. 2022;13(3):363. doi: 10.3390/mi13030363
  102. Narodoslawsky M, Krotscheck C. The sustainable process index (SPI): evaluating processes according to environmental compatibility. J Hazard Mater. 1995;41(2):383-397. doi: 10.1016/0304-3894(94)00114-V
  103. Kokare S, Oliveira JP, Godina R. Life cycle assessment of additive manufacturing processes: a review. J Manuf Syst. 2023;68:536-559. doi: 10.1016/j.jmsy.2023.05.007
  104. Finnveden G, Potting J. Life cycle assessment. In: Wexler P, ed. Encyclopedia of Toxicology (Third Edition). Academic Press. Elsevier, Oxford, United Kingdom; 2014:74-77.
  105. Luengo-Valderrey M-J, Pando-García J, Periáñez-Cañadillas I, Cervera-Taulet A. Analysis of the impact of the triple helix on sustainable innovation targets in spanish technology companies. Sustainability. 2020;12(8):3274. doi: 10.3390/su12083274
  106. Hakeem MM, Chin GH, Frendy, Ito H. Regional sustainable development using a Quadruple Helix approach in Japan. Regl Stud Regl Sci. 2023;10(1):119-138. doi: 10.1080/21681376.2023.2171313
  107. Ektefaie Y, Shen A, Bykova D, Marin MG, Zitnik M, Farhat M. Evaluating generalizability of artificial intelligence models for molecular datasets. Nat Mach Intell. 2024;6(12):1512-1524. doi: 10.1038/s42256-024-00931-6
  108. Liu S, Chen Y, Wang Z, et al. The cutting-edge progress in bioprinting for biomedicine: principles, applications, and future perspectives. MedComm. 2024;5(10):e753. doi: 10.1002/mco2.753
  109. Goetz L, Seedat N, Vandersluis R, van der Schaar M. Generalization—a key challenge for responsible AI in patient-facing clinical applications. NJP Digit Med. 2024;7(1):126. doi: 10.1038/s41746-024-01127-3
  110. Bonatti AF, Vozzi G, Chua CK, Maria CD. A deep learning quality control loop of the extrusion-based bioprinting process. IJB. 2022;8(4):620. doi: 10.18063/ijb.v8i4.620
  111. Seol Y-J, Kang H-W, Lee SJ, Atala A, Yoo JJ. Bioprinting technology and its applications. Eur J Cardiothorac Surg. 2014;46(3):342-348. doi: 10.1093/ejcts/ezu148
  112. Mahadik B, Margolis R, McLoughlin S, et al. An open-source bioink database for microextrusion 3D printing. Biofabrication. 2023;15(1):015008. doi: 10.1088/1758-5090/ac933a
  113. Saalfeld S, Cardona A, Hartenstein V, Tomančák P. CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics. 2009;25(15): 1984-1986. doi: 10.1093/bioinformatics/btp266
  114. Finny AS. 3D bioprinting in bioremediation: a comprehensive review of principles, applications, and future directions. Peer J. 2024;12:e16897. doi: 10.7717/peerj.16897
  115. Almadan A, Li W, Al Ibrahim M. Transfer learning with domain adaptation for palynological image segmentation. Microsc Microanal. 2023;29(Supplement_1):1898-1899. doi: 10.1093/micmic/ozad067.980
  116. Camacho-Gomez D, Sorzabal-Bellido I, Ortiz-de-Solorzano C, Garcia-Aznar JM, Gomez-Benito MJ. A hybrid physics-based and data-driven framework for cellular biological systems: application to the morphogenesis of organoids. iScience. 2023;26(7):107164. doi: 10.1016/j.isci.2023.107164
  117. Kwon JS-I. Adding big data into the equation. Nat Chem Eng. 2024;1(11):724-724. doi: 10.1038/s44286-024-00142-1
  118. Erps T, Foshey M, Luković MK, et al. Accelerated discovery of 3D printing materials using data-driven multiobjective optimization. Sci Adv. 2021;7(42):eabf7435. doi: 10.1126/sciadv.abf7435
  119. Margarita A, Gugliandolo SG, Santoni S, Moscatelli D, Colosimo BM. A novel solution for real-time in-situ cell distribution monitoring in 3D bioprinting via fluorescence imaging. Biofabrication. 2025;17(2):021001. doi: 10.1088/1758-5090/adb891
  120. Kaswan KS, Dhatterwal JS, Batra R, Balusamy B, Gangadevi E. 3D bioprinting technology optimization using machine learning. Comput Intell Bioprint. 2024:303-321.
  121. Chen H, Hardwick J, Gao L, Plasencia DM, Subramanian S, Hirayama R. Acoustics in additive manufacturing: a path toward contactless, scalable, and high-precision manufacturing. Appl Phys Revi. 2025;12(3):031305. doi: 10.1063/5.0271688
  122. El Bouchefry K, de Souza RS. Chapter 12—learning in big data: introduction to machine learning. In: Škoda P, Adam F, eds. Knowledge Discovery in Big Data from Astronomy and Earth Observation. Elsevier, Amsterdam, The Netherlands; 2020:225-249.
  123. Jia A, Chee Kai C, Vladimir M. Application of machine learning in 3D bioprinting: focus on development of big data and digital twin. IJB. 2021;7(1):342. doi: 10.18063/ijb.v7i1.342.
  124. To TT, Al Mahmud A, Ranscombe C. Teaching sustainability using 3D printing in engineering education: an observational study. Sustainability. 2023;15(9):7470. doi: 10.3390/su15097470
  125. Strielkowski W, Grebennikova V, Lisovskiy A, Rakhimova G, Vasileva T. AI-driven adaptive learning for sustainable educational transformation. Sustain Dev. 2024;33(2):1921-1947. doi: 10.1002/sd.3221



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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing
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