Postoperative data leakage in AI-based IOL power prediction formulas: a hidden source of bias
Bibliographic record
Abstract
This study addresses a critical bias in non-optical formulas purely based on AI models and designed to predict the implanted power using the postoperative spherical equivalent (SE) as an input. Two predictive models were developed using the XGBoost algorithm on a training set of 4588 eyes implanted with Finevision IOLs. One model (AI Pred. SE) was trained to predict the postoperative SE from the implanted power and biometric data, while the second model (AI Pred. Power) was designed to predict the implanted IOL power using the achieved SE and biometric data. Both models were evaluated on an independent testing set using standard performance metrics. The logic of the optical behavior was then assessed for each model, by varying the relevant input (SE for AI Pred. Power, Power for AI Pred. SE) across a broad range of values. On standard evaluation, the AI Pred. Power model showed the lowest prediction error (Friedman test p < 0.0001), outperforming conventional formula. However, despite its accuracy in predicting the implanted IOL power, this architecture returned a predicted power that was largely invariant and non-physiologically responsive to changes in the target refraction regardless of the target refraction entered as an input, for a given eye. This finding underscores the importance of excluding real postoperative refractive outcomes from any evaluation processes or formula design. We hypothesize that this common evaluation paradigm for pure-AI formulas creates a fundamental and undetectable bias, producing models that are optimized for retrospective prediction but are incapable of prospective clinical application.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".