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International Journal of Bioprinting AI for sustainable bioprinting

alignment at 30 Hz, and low-force application not only (ii) Material efficiency: Minimization of material
improved bioprinting efficiency but also enhanced resource consumption and waste through efficient deposition
conservation and sustainability by reducing biomaterial or improving formulation success rates.
waste and optimizing printing parameters in real time. (iii) Process efficiency: Optimization of printing
This AI-driven, closed-loop approach exemplifies the parameters (e.g., speed, temperature, pressure) to
potential of intelligent bioprinting systems to advance eco- reduce errors, enhance energy/resource use during
friendly, high-precision fabrication strategies, contributing fabrication, or improve throughput.
to the sustainable evolution of regenerative medicine and
biomedical engineering. (iv) Experimental productivity: Application of ML
to reduce empirical trial-and-error, accelerate
Zboinska et al. investigated the robotic 3D printing parameter tuning, and improve data yield per
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of cellulose nanofibril-alginate hydrogel membranes for experimental cycle.
sustainable architectural applications, analyzing the impact
of toolpath design and ambient drying on structural 4. Discussion
integrity, shrinkage, and aesthetics. Results showed that
solid deposition with multiple layers led to high shrinkage 4.1. Challenges and potential solutions
(~31%) and deformation, while lattice deposition with The integration of AI into sustainable bioprinting has
high porosity reduced shrinkage (~8%) and improved significantly advanced material development, bioink
dimensional stability. Asymmetric toolpaths caused non- screening, processing parameter optimization, and
uniform distortions, and ambient drying significantly intelligent printing. Despite these advancements,
affected membrane curvature and flexibility. To enhance challenges remain in realizing the full potential of AI
scalability and efficiency, classical AI-driven automation in sustainable bioprinting. Robust AI models require
can optimize toolpath generation, extrusion control, large, diverse, and well-annotated data 99,100 ; however,
and drying conditions, ensuring consistent deposition, existing datasets frequently suffer from inconsistency in
reducing material waste, and improving sustainability. parameter reporting and limited data annotation. This
complexity arises from the extensive variability in bioink
AI-assisted intelligent printing marks a significant shift compositions, bioprinting techniques, and biological
toward adaptive, real-time, and sustainable bioprinting systems involved. 74,101 Additionally, standardized metrics
systems. By integrating ML, computer vision, and robotic to comprehensively measure sustainability in bioprinting
control, these systems enable precise material deposition are currently lacking. While several general frameworks
on complex or dynamic substrates while minimizing exist for sustainable manufacturing like the sustainable
human intervention, error, and material waste. Collectively, process index (ecological impact of industrial processes),
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these innovations contribute to greater print success rates, life cycle assessment (environmental impacts of a product’s
reproducibility, and resource efficiency. However, current life cycle), 103,104 and helix of sustainability (industrial raw
implementations often remain platform-specific and material use and reuse onto natural processes), 105,106 their
are tailored to narrow material or geometric constraints, direct application to bioprinting remains limited due to
limiting broader applicability. Moving forward, research the field’s unique processes and materials. This absence
should focus on developing generalizable AI control of universally accepted standards hinders systematic
frameworks, standardizing performance evaluation assessment and comparison of sustainable practices,
metrics, and improving cross-platform interoperability. complicating the optimization of sustainability efforts
Additionally, expanding the use of real-time feedback within bioprinting processes.
loops, multimodal sensing, and automated decision-
making will be key to achieving fully autonomous and Another significant challenge is the generalizability of
clinically scalable intelligent bioprinting systems. AI models across various materials, bioprinting methods,
and evolving conditions. 107,108 AI systems typically perform
Table 1 presents a comparative overview of recent
studies integrating AI or ML with 3D bioprinting, evaluated well within the confines of their training datasets but
sometimes struggle when confronted with new, unfamiliar,
through the lens of sustainability. Each study is classified or dynamically changing scenarios. Factors contributing
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by the method used, materials and printing technique to this limitation include data scarcity for novel materials,
employed, and the relevant sustainability contributions. the complexity of accurately modeling intricate multi-
The following sustainability indicators are used:
material interactions, and the variability in environmental
(i) Sustainable bioink use: Utilization of bioinks derived and operational conditions during bioprinting. Addressing
from renewable, recyclable, or biodegradable sources these issues will require enhanced AI methodologies,
to reduce environmental impact at the material level. robust validation strategies, and diverse training datasets

Volume 11 Issue 4 (2025) 145 doi: 10.36922/IJB025170164