ReviewBurns & trauma2024
Harnessing the power of machine learning into tissue engineering: current progress and future prospects.
Review in Burns & trauma, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Application and prospect of zinc oxide nanoparticle-enhanced biomimetic hydrogels in articular cartilage repair.Biochemistry and biophysics reports · 2026Review
- Review
- When Bioengineering Met Artificial Intelligence: From Machine Learning to Intelligent Bioengineering Systems.Bioengineering (Basel, Switzerland) · 2026Article
- Next-Generation Surgery: Integrating Artificial Intelligence, Genetic Technologies, Bioengineering and Rehabilitation Into Modern Practices.Exploration (Beijing, China) · 2026Article
- Amphibian-derived peptides as novel therapeutics for skin wound healing: Mechanisms, applications, and challenges.Zoological research · 2026Review
- Advanced Grafting Biomaterials and Technologies in Chronic Wound Care: Mechanisms, Clinical Outcomes, and Therapeutic Integration.Journal of functional biomaterials · 2026Review
- Advances in Biomaterials for Tissue Regeneration: From Scaffold Design to CAP-Enabled Interfaces and AI-Driven Optimization.Biomimetics (Basel, Switzerland) · 2026Review
- Soft Tissue Scaffolds in Breast Reconstruction: Evolution from Acellular Dermal Matrices to Synthetic Polymers.Journal of clinical medicine · 2026Review
- Green and Scalable Manufacturing of Biodegradable Polymer Scaffolds: Solvent-Free Processing, Supercritical COPolymers · 2026Review
- Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling for biomedical engineering.Materials today. Bio · 2026Review
- 3D biofabricated in vitro models as new approach methodologies for animal alternatives.npj biomedical innovations · 2026Review
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- Advancing the 3Rs in bone tissue engineering: emergingFrontiers in physiology · 2026Review
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Harnessing Ribonucleoprotein Granule Biology for Cancer Therapy: The Central Role of Protein Modifications.Research (Washington, D.C.) · 2026Review
- Spatial patterning strategies for liver tissue engineering: Biofabrication technologies and applications.Advanced drug delivery reviews · 2026Review
- Artificial intelligence in tissue engineering and regenerative medicine: from algorithmic capability to intelligent integration.Frontiers in bioengineering and biotechnology · 2026Review
- Acellular Extracellular Matrix Scaffolds in Regenerative Medicine: Advances in Decellularization and Clinical Applications.Journal of functional biomaterials · 2025Review
- Advancing Scaffold Architecture for Bone Tissue Engineering: A Comparative Study of 3D-Printed β-TCP Constructs in Dynamic Culture with pBMSC.Journal of functional biomaterials · 2025Article
- Management of Burns: Multi-Center Assessment Comparing AI Models and Experienced Plastic Surgeons.Journal of clinical medicine · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tissue engineering is a discipline based on cell biology and materials science with the primary goal of rebuilding and regenerating lost and damaged tissues and organs. Tissue engineering has developed rapidly in recent years, while scaffolds, growth factors, and stem cells have been successfully used for the reconstruction of various tissues and organs. However, time-consuming production, high cost, and unpredictable tissue growth still need to be addressed. Machine learning is an emerging interdisciplinary discipline that combines computer science and powerful data sets, with great potential to accelerate scientific discovery and enhance clinical practice. The convergence of machine learning and tissue engineering, while in its infancy, promises transformative progress. This paper will review the latest progress in the application of machine learning to tissue engineering, summarize the latest applications in biomaterials design, scaffold fabrication, tissue regeneration, and organ transplantation, and discuss the challenges and future prospects of interdisciplinary collaboration, with a view to providing scientific references for researchers to make greater progress in tissue engineering and machine learning.
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.