Toward autonomous discovery: agentic AI and the future of ophthalmic research

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 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.

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.