Integrating AI with tele-ophthalmology in Canada: a review
Bibliographic record
Abstract
The field of ophthalmology is rapidly advancing, with technological innovations enhancing the diagnosis and management of eye diseases. Tele-ophthalmology, or the use of telemedicine for ophthalmology, has emerged as a promising solution to improve access to eye care services, particularly for patients in remote or underserved areas. Despite its potential benefits, tele-ophthalmology faces significant challenges, including the need for high volumes of medical images to be analyzed and interpreted by trained clinicians. Artificial intelligence (AI) has emerged as a powerful tool in ophthalmology, capable of assisting clinicians in diagnosing and treating a variety of conditions. Integrating AI models into existing tele-ophthalmology infrastructure has the potential to revolutionize eye care services by reducing costs, improving efficiency, and increasing access to specialized care. By automating the analysis and interpretation of clinical data and medical images, AI models can reduce the burden on human clinicians, allowing them to focus on patient care and disease management. Available literature on the current status of tele-ophthalmology in Canada and successful AI models in ophthalmology was acquired and examined using the Arksey and O'Malley framework. This review covers literature up to 2022 and is split into 3 sections: 1) existing Canadian tele-ophthalmology infrastructure, with its benefits and drawbacks; 2) preeminent AI models in ophthalmology, across a variety of ocular conditions; and 3) bridging the gap between Canadian tele-ophthalmology and AI in a safe and effective manner.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".