Recent advances in smart contact lenses
Bibliographic record
Abstract
The growing demand for healthcare services and recent advances in materials and biosensing technologies have accelerated the development of point-of-care (POC) diagnostics, with smart contact lenses (SmCLs) emerging as a promising platform. This review systematically evaluates the role of tear fluid as a noninvasive diagnostic medium, highlighting its biochemical composition and challenges in sample collection. We examine the design parameters essential for functional SmCLs, including biocompatibility, oxygen permeability, wettability, and mechanical properties, which collectively determine long-term comfort and device performance. SmCLs can significantly improve the bioavailability of drug delivery while addressing the limitations of traditional ocular treatments, such as the rapid dissipation of eye medications through the nasolacrimal duct. Furthermore, the potential of SmCLs in disease diagnosis through chemical and physical biomarker detection is highlighted, showcasing their ability to monitor glucose levels and intraocular pressure in real-time. Overall, current evidence supports SmCLs as multifunctional devices capable of combining diagnostics and therapy in real time. However, large-scale validation studies are required to establish clinical accuracy, patient adherence, and cost-effectiveness. This review concludes that SmCLs represents an innovative direction in personalized healthcare, integrating materials science, biosensing, and drug delivery for noninvasive, continuous health monitoring of ocular and system diseases. • Tear fluid enables noninvasive, real-time diagnostics using smart contact lenses. • SmCLs offer enhanced drug delivery, addressing limitations of eye drop treatments. • Advances in materials improve biocompatibility, comfort, and long-term wearability. • Integrated sensors in SmCLs can monitor glucose levels and intraocular pressure. • Clinical translation requires validation of accuracy, efficacy, and cost-effectiveness.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".