ArticleInternational journal of bioprinting2023
Machine learning and 3D bioprinting.
Article in International journal of bioprinting, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- When Bioengineering Met Artificial Intelligence: From Machine Learning to Intelligent Bioengineering Systems.Bioengineering (Basel, Switzerland) · 2026Article
- Comparative Evaluation of Machine Learning-Assisted Statistical Modeling of Biopolymer Hydrogel Swelling for Material Optimization.ACS omega · 2026Article
- AI-Guided 4D Printing of Carnivorous Plants-Inspired Microneedles for Accelerated Wound Healing.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- From Technological Innovation to Clinical Translation: Progress and Challenges in 3D Bioprinting for the Development of Breast Cancer Bone Metastasis Models.Advanced healthcare materials · 2026Review
- Artificial intelligence empowered biomaterials for cancer therapy: From rational design to clinical translation.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- A Formal Optimization-Oriented Design Framework for Predictive Extrusion-Based 3D Bioprinting.Biomimetics (Basel, Switzerland) · 2026Article
- Three-tier framework for high-throughput biofabrication: Integrating 3D bioprinting, assistive platforms, and translational opportunities.Bioactive materials · 2026Review
- Unconventional bioprinting modalities for advanced tissue biofabrication.Biomaterials · 2026Review
- Advances in 3D Bioprinting: Materials, Processes, and Emerging Applications.Micromachines · 2026Review
- Quantitative Assessment of Hydrogel Printability in Extrusion Bioprinting.Gels (Basel, Switzerland) · 2026Review
- Acoustic Bioprinting: A Glimpse Into an Emerging Field.Small methods · 2026Review
- Three-dimensional bioprinted organoids: advances and clinical translation.Regenerative biomaterials · 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
- 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
- 3D Bioprinting and Artificial Intelligence for Tumor Microenvironment Modeling: A Scoping Review of Models, Methods, and Integration Pathways.Molecular pharmaceutics · 2025Article
- Data-Driven Optimization of Bioink Formulations for Extrusion-Based Bioprinting: A Predictive Modeling Approach.Journal of manufacturing science and engineering · 2025Article
- Converging Architectures: Precision Biomanufacturing and Soft Robotics Rewiring Tissue Engineering.Micromachines · 2025Article
- Machine Learning in Predicting and Optimizing Polymer Printability for 3D Bioprinting.Polymers · 2025Review
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- 3D bioprinting in bioremediation: a comprehensive review of principles, applications, and future directions.PeerJ · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
48With the growing number of biomaterials and printing technologies, bioprinting has brought about tremendous potential to fabricate biomimetic architectures or living tissue constructs. To make bioprinting and bioprinted constructs more powerful, machine learning (ML) is introduced to optimize the relevant processes, applied materials, and mechanical/biological performances. The objectives of this work were to collate, analyze, categorize, and summarize published articles and papers pertaining to ML applications in bioprinting and their impact on bioprinted constructs, as well as the directions of potential development. From the available references, both traditional ML and deep learning (DL) have been applied to optimize the printing process, structural parameters, material properties, and biological/mechanical performance of bioprinted constructs. The former uses features extracted from image or numerical data as inputs in prediction model building, and the latter uses the image directly for segmentation or classification model building. All of these studies present advanced bioprinting with a stable and reliable printing process, desirable fiber/droplet diameter, and precise layer stacking, and also enhance the bioprinted constructs with better design and cell performance. The current challenges and outlooks in developing process-material-performance models are highlighted, which may pave the way for revolutionizing bioprinting technologies and bioprinted construct design.
Indexed as
Identifiers
What Socratic 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 Socratic graph.