Poster Session I: Leveraging AI to accelerate scientific discoveries
Bibliographic record
Abstract
We introduce a structured approach that leverages AI to accelerate scientific discoveries. We showcase the efficacy of this technique via a proof-of-concept study identifying markers of sex in retinal images. Our methodology consists of four stages: In Phase 1, CNN development, we train a VGG model to recognize patient sex from retinal images. Phase 2, Inspiration, involves reviewing post-hoc interpretation tools' visualizations to draw observations and formulate exploratory hypotheses regarding the CNN model's decision process. This yielded 14 testable hypotheses related to potential variances in vasculature and optic disc. In Phase 3, Exploration, we test these hypotheses on an independent dataset, of which nine demonstrated significant differences. In Phase 4, Verification, five out of nine these nine hypotheses are re-tested on a new dataset, verifying five of them: significantly greater length, more nodes and branches of retinal vasculature, a larger area covered by vessels in the superior temporal quadrant, and a darker peri-papillary region in male eyes. Finally, we conducted a psychophysical study and trained a group of ophthalmologists (N=26) to identify these new retinal features for sex classification. Their performance, initially on par with chance and a non-expert group (N=31), significantly improved post-training (p<.001, d=2.63). These outcomes illustrate the potential of our methodology in leveraging AI applications for retinal biomarker discovery.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
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".