Smart hydrogels for tissue engineering and regenerative medicine: how far have we come
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it