ArticleAdvanced functional materials2025
The Synergy of Artificial Intelligence and 3D Bioprinting: Unlocking New Frontiers in Precision and Tissue Fabrication.
Article in Advanced functional materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Advanced cell-instructive bio-inks: Engineering multiscale niches for functional tissue formation.Bioactive materials · 2027Review
- Recent advances in MOF-based room-temperature gas sensors: dimensional nanostructure and advanced transduction mechanisms.Mikrochimica acta · 2026Review
- 3D-Printed Bioadhesive Interfaces as Proliferative Niches for Tissue Repair.Cell proliferation · 2026Review
- Next-generation epidermal patches: Bridging 3D and multidimensional printing for biomedical and personal care innovations.Bioactive materials · 2026Review
- Review
- 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
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (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
- Quantitative Assessment of Hydrogel Printability in Extrusion Bioprinting.Gels (Basel, Switzerland) · 2026Review
- Rebuilding the degenerative disc microenvironment: mesenchymal stem cells, exosomes, and bioengineered scaffolds.Frontiers in bioengineering and biotechnology · 2026Review
- Bioinks in systems biology: integrating biomaterial design with cellular network modelling for predictive biofabrication.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
- AI-Integrated Micro/Nanorobots for Biomedical Applications: Recent Advances in Design, Fabrication, and Functions.Biosensors · 2025Review
- Reproducibility roadblocks and standardization in carbon dot synthesis: a critical review of current practices, challenges, and future directions.Mikrochimica acta · 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
11 authors.
Funding
Abstract
This Review examines the transformative role of artificial intelligence (AI) in 3D bioprinting, focusing on how advanced AI technologies enhance its precision, functionality, and scalability. AI, through branches, such as machine learning (ML), computer vision (CV), robotics, natural language processing and expert systems, provides critical improvements in real-time process monitoring, error correction, and optimization of bioprinting parameters. The integration of AI enables automated quality control and predictive maintenance, improving bioprinting outcomes by increasing cell viability and structural fidelity, and reducing the amount of bioink wasted. Specifically, ML algorithms are employed to predict optimal bioprinting conditions and streamline the bioprinting workflow, while deep learning enhances the ability to process complex datasets for precision tissue biofabrication. Furthermore, AI-powered robotics and CV systems ensure accurate bioink placement and facilitate the construction of complex tissues. Despite the remarkable progress, challenges remain, particularly in the areas of process monitoring, quality control, and the scalability of bioprinting systems. This Review also aims to guide scientists, engineers, and healthcare providers in understanding the complexities and potential of AI-enhanced bioprinting, fostering a deeper appreciation of its role in the future of regenerative medicine and personalized healthcare.
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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.