The Role of Artificial Intelligence in Triaging Patients in Eye Casualty Departments: A Systematic Review
Bibliographic record
Abstract
Visual impairment and eye disease remain a significant burden, highlighting the need for further support regarding eye care services. Artificial intelligence (AI) and its rapid advancements are providing an avenue for transforming healthcare. As a result, this provides a potential avenue to address the growing challenges with eye health and could assist in settings such as eye casualty departments. This review aims to evaluate current studies on AI implementation in eye casualty triage to understand the potential application for the future. A systematic review was conducted across a range of sources and databases producing 77 records initially identified, with four studies included in the final analysis. The findings demonstrated that AI tools are able to produce consistent and accurate triaging of patients and provide improvement in work efficiency without compromising safety. However, we note limitations of the studies including limited external validations of results and general applicability at present. Additionally, all the studies highlight the need for further studies and testing to allow for better understanding and validation of AI tools in eye casualty triaging.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".