Toward autonomous discovery: agentic AI and the future of ophthalmic research
Bibliographic record
Abstract
PURPOSE OF REVIEW: Rapid advances in large language models (LLMs) have led to the emergence of agentic artificial intelligence (AI) systems capable of autonomously performing complex scientific tasks. This review examines recent developments in agentic AI, highlighting their transformative potential for ophthalmology research and clinical practice, and discusses associated ethical considerations. RECENT FINDINGS: Recent studies demonstrate that agentic AI systems can autonomously execute tasks traditionally performed by human researchers, including peer review, hypothesis generation, systematic reviews, and experimental design. Notable examples include AI-generated manuscripts accepted through peer review, automated systematic reviews outperforming human accuracy and efficiency, and performing complex biomedical analyses across diverse domains. Although direct ophthalmology-specific applications remain nascent, the field's data-rich nature positions it ideally for adopting agentic AI in several areas such as automated chart review, health economics modeling, and enhanced image analysis. SUMMARY: Agentic AI represents a paradigm shift in scientific research, offering significant opportunities to enhance productivity, rigor, and innovation in ophthalmology. However, integration into clinical and research workflows necessitates careful consideration of ethical issues, including authorship attribution, data privacy, bias mitigation, and accountability. Clear governance frameworks, rigorous validation standards, and interdisciplinary training will be essential to responsibly harness agentic AI in ophthalmology.
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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 | 0.030 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".