ReviewTissue engineering. Part A2023
Machine Learning in Tissue Engineering.
Review in Tissue engineering. Part A, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Virtual histopathology methods in medical imaging - a systematic review.BMC medical imaging · 2024Pooled it
- Machine learning-guided discovery of a bioactive hydrogel for spatiotemporally orchestrated tendon-to-bone healing.Bioactive materials · 2026Article
- Review
- Mechanics-AI: A Bio-Inspired Physics Intelligence Pipeline for Cross-Domain Engineering Prediction and Sustainable Design.Biomimetics (Basel, Switzerland) · 2026Article
- Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling for biomedical engineering.Materials today. Bio · 2026Review
- Artificial intelligence in tissue engineering and regenerative medicine: from algorithmic capability to intelligent integration.Frontiers in bioengineering and biotechnology · 2026Review
- Biomimetic gradient hydrogels for osteochondral regeneration: from multi-dimensional design to clinical translation.Frontiers in bioengineering and biotechnology · 2026Review
- AI dialogues in cartilage repair: which guides evidence-based decisions better?Frontiers in cell and developmental biology · 2026Article
- Towards a bioengineered airway: advances in tracheal tissue engineering and biofabrication.Frontiers in cell and developmental biology · 2026Review
- Interpretable machine learning-driven optimization of physicochemical properties in hydrogel scaffolds to promote macrophage polarization.Biomaterials translational · 2026Article
- Recent Advances in Periodontal Regenerative Medicine: A Focus on the Role of Mechanical Stimulation.Biomedicines · 2025Review
- Immunomodulatory Delivery Materials for Tissue Repair.Advanced healthcare materials · 2025Review
- Tendon regeneration deserves better: focused review onFrontiers in bioengineering and biotechnology · 2025Review
- Artificial intelligence-assisted organoid construction in congenital heart disease: current applications and future prospects.Frontiers in bioengineering and biotechnology · 2025Review
- Mapping Biomaterial Complexity by Machine Learning.Tissue engineering. Part A · 2024Review
- Hematoxylin and Eosin Architecture Uncovers Clinically Divergent Niches in Pancreatic Cancer.Tissue engineering. Part A · 2024Article
- Artificial Intelligence (AI): A Potential Game Changer in Regenerative Orthopedics-A Scoping Review.Indian journal of orthopaedics · 2024Article
- Modelling and targeting mechanical forces in organ fibrosis.Nature reviews bioengineering · 2024Article
- Trends in bioactivity: inducing and detecting mineralization of regenerative polymeric scaffolds.Journal of materials chemistry. B · 2024Review
- Three-Dimensional Printing Methods for Bioceramic-Based Scaffold Fabrication for Craniomaxillofacial Bone Tissue Engineering.Journal of functional biomaterials · 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
3 authors.
Funding
Abstract
Machine learning (ML) and artificial intelligence have accelerated scientific discovery, augmented clinical practice, and deepened fundamental understanding of many biological phenomena. ML technologies have now been applied to diverse areas of tissue engineering research, including biomaterial design, scaffold fabrication, and cell/tissue modeling. Emerging ML-empowered strategies include machine-optimized polymer synthesis, predictive modeling of scaffold fabrication processes, complex analyses of structure-function relationships, and deep learning of spatialized cell phenotypes and tissue composition. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex and multivariate analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research. Impact statement Machine learning (ML) has accelerated scientific discovery and augmented clinical practice across multiple fields. Now, ML has driven exciting new paradigms in tissue engineering research, including machine-optimized biomaterial design, predictive modeling of scaffold fabrication, and spatiotemporal analysis of cell and tissue systems. The emergence of ML in tissue engineering, while relatively recent, has already enabled increasingly complex analyses of the relationships between biological, chemical, and physical factors in driving tissue regenerative outcomes. This review highlights the novel methodologies, emerging strategies, and areas of potential growth within this rapidly evolving area of research.
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.