ReviewFrontiers in bioengineering and biotechnology2022
Bioink Formulation and Machine Learning-Empowered Bioprinting Optimization.
Review in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 85 citations in OpenAlex.
- Review
- Next-Generation Bioinks in 3D Bioprinting: Advances, Challenges, and Emerging Opportunities.ACS omega · 2026Review
- Harnessing the Bio-Instructive Placental Extracellular Matrix: Structural Properties, Decellularization, and Applications in Regenerative Medicine.International journal of molecular sciences · 2026Review
- Emerging Nano Bioinks in Bioprinting: Functional Materials, Engineering Strategies, and Biomedical Applications.Materials (Basel, Switzerland) · 2026Review
- The Next Phase of 3D Bioprinting: AI-Native Systems-A Narrative Review.Journal of functional biomaterials · 2026Review
- Physical and Mechanical Characterisation of 3D-Bioprinted Hydrogels for Dental Applications: A Scoping Review.Gels (Basel, Switzerland) · 2026Review
- Modeling of Biomechanical and Functional Parameters of Hydrogel-Cell Composites Fabricated by 3D Bioprinting Using AI-Supported Approach.Materials (Basel, Switzerland) · 2026Article
- Functional Nano-to-Microstructures by Jet Printing and Direct Ink Writing.Chemical reviews · 2026Review
- Acoustic Bioprinting: A Glimpse Into an Emerging Field.Small methods · 2026Review
- Artificial intelligence-assisted smart hydrogel bioinks in 3D bioprinting: design, optimization, and construct validation for functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Three-dimensional bioprinted organoids: advances and clinical translation.Regenerative biomaterials · 2026Review
- Advances in 3D Printed Scaffolds for Periodontal Regeneration.Current oral health reports · 2026Review
- Hydrogel-based smart bioinks for 3D bioprinting: design strategies and applications in brain tissue engineering and tumor microenvironment modelling.BioImpacts : BI · 2026Review
- Bioprinting for craniofacial reconstruction: A review of advancements, clinical use, and challenges.Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery · 2025Review
- Possible Diagnostic and Therapeutic Applications of Bioprinting for Bone Regeneration in Maxillofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- The Synergy of Artificial Intelligence and 3D Bioprinting: Unlocking New Frontiers in Precision and Tissue Fabrication.Advanced functional materials · 2025Article
- Integrating Decision Trees and Clustering for Efficient Optimization of Bioink Rheology and 3D Bioprinted Construct Microenvironments.Journal of manufacturing science and engineering · 2025Article
- Rheological, Structural, and Biological Trade-Offs in Bioink Design for 3D Bioprinting.Gels (Basel, Switzerland) · 2025Review
- Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization.Gels (Basel, Switzerland) · 2025Review
- Machine Learning in Predicting and Optimizing Polymer Printability for 3D Bioprinting.Polymers · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bioprinting enables the fabrication of complex, heterogeneous tissues through robotically-controlled placement of cells and biomaterials. It has been rapidly developing into a powerful and versatile tool for tissue engineering. Recent advances in bioprinting modalities and biofabrication strategies as well as new materials and chemistries have led to improved mimicry and development of physiologically relevant tissue architectures constituted with multiple cell types and heterogeneous spatial material properties. Machine learning (ML) has been applied to accelerate these processes. It is a new paradigm for bioprinting. In this review, we explore current trends in bioink formulation and how ML has been used to accelerate optimization and enable real-time error detection as well as to reduce the iterative steps necessary for bioink formulation. We examined how rheometric properties, including shear storage, loss moduli, viscosity, shear-thinning property of biomaterials affect the printability of a bioink. Furthermore, we scrutinized the interplays between yield shear stress and the printability of a bioink. Moreover, we systematically surveyed the application of ML in precision in situ surgical site bioprinting, closed-loop AI printing, and post-printing optimization.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.