Smart hydrogels for tissue engineering and regenerative medicine: how far have we come
Bibliographic record
Abstract
Smart hydrogels have become precision platforms that interact with complex biological cues. We formalize a 2025 definition, materials that sense a clinically relevant cue and reproducibly execute a specified, reversible function under physiologic conditions, and introduce a unified, feature-based, three-tier framework: Responsive (open-loop cue and response), Adaptive (multi-cue or stateful), and Intelligent (closed-loop sense, decide, and act). This review captures momentum from 2020 to 2025, a period marked by clinical and innovative breakthroughs, FDA-cleared formulations, and integration of advanced technologies, including AI-assisted design, fourth-dimensional (4D) bioprinting, and biohybrid interfaces. We spotlight cutting-edge developments in programmable degradation, self-healing, and multi-stimuli responsiveness, alongside emerging hydrogel fabrication strategies such as nanoparticle (NP)-laden bioinks and in situ light-activated crosslinking. Although barriers to regulation and translation remain, cross-disciplinary efforts with a sustainability- and ethics-first mind-set are redefining these materials’ capabilities. Smart hydrogels are no longer just innovative, researchers in tissue engineering and regenerative medicine are actively redefining both their clinical potential and what it means for a material to be “smart.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".