Eye-XAI: an explainable artificial intelligence approach for eye disease detection using symptom analysis
Bibliographic record
Abstract
The early and accurate detection of eye diseases play a pivotal role in preventing vision loss and improving patients’ quality of life. It is therefore important to search for methods for improving this detection. It turns out that Artificial Intelligence (AI) has shown great promise for the detection task. However, AI models are often opaque and complex and as a result there has been a slow adoption for these models in clinical settings. In this paper Eye-XAI is introduced which provides an effective approach to the detection combining Explainable Artificial Intelligence (XAI) techniques with symptom analysis to enhance the transparency and interpretability of eye disease detection models. Our results demonstrate that Eye-XAI not only achieves high accuracy (99.11%) for eye disease detection but also provides transparent and interpretable insights into the diagnostic process. The adoption of Eye-XAI can therefore significantly enhance the early detection and management of eye diseases while empowering clinicians with a deeper understanding of its AI-based diagnostic recommendations. Furthermore, this approach promotes patient engagement by facilitating communication and trust between patients and their healthcare providers. Eye-XAI represents a major step towards the integration of XAI in ophthalmology, unlocking new possibilities for improved eye disease diagnosis and treatment.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".