Artificial intelligence oculomics for systemic health and longevity medicine: 2025 and beyond
Bibliographic record
Abstract
PURPOSE OF REVIEW: With the rise of 'oculomics' and the application of advanced artificial intelligence techniques in healthy ageing, retinal imaging, the only way we can directly visualize the microvascular circulation, is expanding beyond ophthalmology into broader systemic health monitoring. The purpose of this review is to summarize recent advances in this rapidly evolving field and assess the opportunities, challenges, and future directions of the use of oculomics in translating into real-world clinical use. RECENT FINDINGS: Retinal imaging modalities, such as color fundus photography, optical coherence tomography (OCT), OCT angiography (OCTA), and wide-field imaging, are increasingly integrated with deep learning algorithms to detect, predict, and manage a broad spectrum of systemic diseases, including cardiovascular, cerebrovascular, renal, metabolic, and neurodegenerative disorders, as well as less commonly studied conditions. While research in more established areas is beginning to address clinical translation and implementation, significant challenges remain before these technologies can be reliably adopted in long-term, real-world healthcare settings. SUMMARY: Artificial intelligence applied to retinal imaging has matured from proof-of-concept classifiers to externally validated, occasionally regulated tools that noninvasively profile systemic conditions. Multiplexed foundation models and multimodal transformers herald a shift toward holistic 'oculomics' platforms, yet prospective multicenter trials, equitable performance auditing, and health-economic evaluations are essential before widescale clinical adoption.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".