Foundation models in ophthalmology: opportunities and challenges
Bibliographic record
Abstract
PURPOSE OF REVIEW: Last year marked the development of the first foundation model in ophthalmology, RETFound, setting the stage for generalizable medical artificial intelligence (GMAI) that can adapt to novel tasks. Additionally, rapid advancements in large language model (LLM) technology, including models such as GPT-4 and Gemini, have been tailored for medical specialization and evaluated on clinical scenarios with promising results. This review explores the opportunities and challenges for further advancements in these technologies. RECENT FINDINGS: RETFound outperforms traditional deep learning models in specific tasks, even when only fine-tuned on small datasets. Additionally, LMMs like Med-Gemini and Medprompt GPT-4 perform better than out-of-the-box models for ophthalmology tasks. However, there is still a significant deficiency in ophthalmology-specific multimodal models. This gap is primarily due to the substantial computational resources required to train these models and the limitations of high-quality ophthalmology datasets. SUMMARY: Overall, foundation models in ophthalmology present promising opportunities but face challenges, particularly the need for high-quality, standardized datasets for training and specialization. Although development has primarily focused on large language and vision models, the greatest opportunities lie in advancing large multimodal models, which can more closely mimic the capabilities of clinicians.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".