ReviewGels (Basel, Switzerland)2025
Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization.
Review in Gels (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- From Processing to Performance: A Multi-Scale Machine Learning Framework for SBS-Modified Asphalt Gels with Multi-Target Correlation and Explainable AI.Gels (Basel, Switzerland) · 2026Article
- Sulfate Attack-Induced C-S-H Gel Degradation Mechanism and Machine Learning-Based Strength Prediction of Coal Gangue Aggregate Concrete.Gels (Basel, Switzerland) · 2026Article
- Functional Materials for Additive Manufacturing: Materials Design, Processing, and Emerging Applications.Nanomaterials (Basel, Switzerland) · 2026Review
- Research Progress on Natural Polysaccharide Hydrogels in the Diagnosis and Treatment of Colorectal Cancer.Gels (Basel, Switzerland) · 2026Review
- Construction of ZnGels (Basel, Switzerland) · 2026Article
- Machine Learning-Based Prediction of Textural Properties and Nonlinear Regulatory Pattern Analysis of 3D-Printed Dough Containing Konjac Glucomannan.Foods (Basel, Switzerland) · 2026Article
- Magnetic Cellulose Nanocrystal Composites: Synthesis, Properties, Applications, and Opportunities.Nanomaterials (Basel, Switzerland) · 2026Review
- Integrating Cells, Biomaterials, and Advanced Engineering for Next-Generation Peripheral Nerve Repair.Cells, tissues, organs · 2026Review
- Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring.Gels (Basel, Switzerland) · 2026Review
- Cellulose-Based Conductive Hydrogels: Design Strategies and Applications in Flexible Electronics.Gels (Basel, Switzerland) · 2026Review
- Tri-domain prediction and optimization of nanocomposite polymers for high-performance 3D printing.Scientific reports · 2026Article
- The Synthetic Extracellular Matrix as a Maestro of the In Vitro Stem Cell Niche: Orchestrating Fate and Function.Biomedicines · 2026Review
- Design and Application of Stimuli-Responsive Hydrogels for 4D Printing: A Review of Adaptive Materials in Engineering.Gels (Basel, Switzerland) · 2026Review
- Additive manufacturing - an antidote to bottlenecks in tissue engineering and regenerative medicine.Frontiers in bioengineering and biotechnology · 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
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- AI-Integrated Micro/Nanorobots for Biomedical Applications: Recent Advances in Design, Fabrication, and Functions.Biosensors · 2025Review
- Manufacturing, Microstructure, and Mechanics of 316L SS Biomaterials by Laser Powder Bed Fusion.Journal of functional biomaterials · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Machine learning is reshaping gel-based additive manufacturing by enabling accelerated material design and predictive process optimization. This review provides a comprehensive overview of recent progress in applying machine learning across gel formulation development, printability prediction, and real-time process control. The integration of algorithms such as neural networks, random forests, and support vector machines allows accurate modeling of gel properties, including rheology, elasticity, swelling, and viscoelasticity, from compositional and processing data. Advances in data-driven formulation and closed-loop robotics are moving gel printing from trial and error toward autonomous and efficient material discovery. Despite these advances, challenges remain regarding data sparsity, model robustness, and integration with commercial printing systems. The review results highlight the value of open-source datasets, standardized protocols, and robust validation practices to ensure reproducibility and reliability in both research and clinical environments. Looking ahead, combining multimodal sensing, generative design, and automated experimentation will further accelerate discoveries and enable new possibilities in tissue engineering, biomedical devices, soft robotics, and sustainable materials manufacturing.
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.