ReviewFundamental research2026
Artificial intelligence for design strategies of tissue engineering materials.
Review in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Advancements in Functional Polymeric Scaffolds for Scar-Free Skin Regeneration.Polymer science & technology (Washington, D.C.) · 2026Review
- Artificial Intelligence and Digital Workflow in Craniofacial Bone Tissue Engineering: From Cone-Beam Computed Tomography (CBCT) to Personalized Bioceramic Implants.Dentistry journal · 2026Review
- Role of polymeric nanocomposite for tissue engineering applications.RSC advances · 2026Review
- Green and Scalable Manufacturing of Biodegradable Polymer Scaffolds: Solvent-Free Processing, Supercritical COPolymers · 2026Review
- Towards a bioengineered airway: advances in tracheal tissue engineering and biofabrication.Frontiers in cell and developmental biology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, artificial intelligence (AI), driven by machine learning (ML) and deep learning (DL) algorithms, has fundamentally transformed the design and performance prediction of tissue engineering materials. By analyzing large amounts of biomaterial data, ML has demonstrated remarkable effectiveness in optimizing mechanical properties, assessing biocompatibility, and enhancing the structural design of tissue engineering materials, while also accurately predicting the complex interactions between materials and cells. Due to its exceptional ability to process nonlinear data, DL is particularly effective in analyzing complex biological data, such as interactions between tissue engineering materials, cells, and proteins, as well as promoting tissue regeneration. This review systematically examines ML- and DL-based design methods for tissue engineering materials and their important applications in recent years. It also explores their technical advantages, challenges, and future directions, providing insights for advancing regenerative medicine.
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.