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International Journal of Bioprinting AI for sustainable bioprinting

sharing, standardization, and the development of open screening to assess their practicality and reliability for
benchmarks to accelerate progress in predictive modeling bioprinting applications. This requires a detailed analysis of
for bioprinting. performance indicators, including printability, rheological

To circumvent these limitations, researchers are behavior, crosslinking efficiency, and cellular response
turning to other AI strategies, such as multi-objective under various printing conditions. This stage ensures that
optimization, that focus on synthesis pathways rather hydrogel formulations not only meet theoretical design
than structural modeling alone. Hardian et al. applied criteria but also perform effectively during bioprinting.
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support vector machines and a multi-objective genetic The integration of AI-driven methodologies, such as
algorithm to optimize the green electrochemical synthesis ML models, enables rapid evaluation and prediction
of zeolitic imidazolate framework-8. Their approach of hydrogel performance, minimizes trial-and-error
enabled simultaneous prediction and optimization of experimentation, and ensures sustainable, high-quality
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synthesis parameters to maximize product quality while outcomes in bioprinting applications.
minimizing environmental impact. The AI-optimized Ink printability is a crucial aspect of 3D printing as it
conditions achieved a high yield of 88%, crystallinity of greatly influences the integration and function of printed
86%, and 100% purity, while keeping energy consumption implants. ML has been used to predict printability from
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at 7 kWh/kg of product, an E-factor of 11 kg waste/kg biomaterial formulations to guide the development
product, and a carbon footprint of 27 kg carbon dioxide- of inks. Chen et al. used ML learning algorithms to
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equivalent/kg product. These results demonstrate the predict printable biomaterial formulations for direct ink
potential of AI to identify synthesis conditions that balance writing. The data used in their study consisted of 210 ink
performance with sustainability. Although focused on formulations with two ink systems: the hydrogel-based
metal–organic frameworks, the methodology is highly system (including both natural and synthetic hydrogels)
transferable to bioprinting contexts such as bioink and and the polymer organic solution-based system. The
scaffold development, where similar multi-objective trade- biomaterials include polymers of a range of molecular
offs between material function and environmental impact weights, properties, and functional fillers with different
must be addressed. sizes and functions. The inks were 3D-printed using the
These advancements represent a transformative direct ink writing technique, and their printability was
opportunity for sustainable hydrogel and biomaterial assessed. The ML algorithms (decision tree, random forest,
discovery in bioprinting. Emerging ML approaches enable and deep learning) successfully predicted the printability
predictive modeling at multiple scales, from atomic- of biomaterial formulations with high accuracy (>88%),
level biomolecular interactions to macroscopic material as shown in Figure 4A. In addition, a printability map of
performance, facilitating the rational design of bioinks biomaterial composites was generated using the trained ML
with optimized mechanical strength, biocompatibility, algorithms to guide the ink design (Figure 4B). This study
and biodegradability. Tools like AlphaFold exemplify how has paved the way for using ML in guiding the selection of
deep learning can predict protein–material interactions to materials with a range of properties for different types of
guide the development of functional bioactive hydrogels. 3D printing.
Complementing this, other AI-driven frameworks have AI-driven models can screen potential hydrogel
demonstrated the ability to optimize synthesis pathways by candidates based on their physicochemical and
balancing material yield with environmental metrics such mechanical properties, such as viscosity, shear-thinning
as energy consumption and carbon footprint. Together, behavior, and crosslinking efficiency. Nadernezhad and
these approaches highlight the broader potential of AI Groll employed a random forest algorithm to predict
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in accelerating sustainable material development for the printability of hyaluronic acid-based hydrogel inks
bioprinting. By integrating high-throughput computational based on their rheological properties. They quantitatively
modeling with multi-objective optimization, AI reduces assessed the significance of various rheological parameters
reliance on trial-and-error experimentation, conserves and identified 13 critical measures that defined the
resources, and enables the targeted design of novel, eco- printability of hydrogel formulations. Their trained model
friendly bioinks, thus contributing to more efficient and statistically predicted that a printable formulation should
sustainable biofabrication. demonstrate high yield viscosity and minimal plasticity
before initiating flow.
3.2. Bioink formulation screening
Once promising hydrogel materials are discovered and Lee et al. presented a ML-based strategy for bioink
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selected based on their functional properties tailored to design, focusing on elastic modulus for shape fidelity
specific applications, the next step is bioink formulation and yield stress for extrusion feasibility (Figure 4B). Data

Volume 11 Issue 4 (2025) 139 doi: 10.36922/IJB025170164