Foundation models in ophthalmology

Area of Science:

• Artificial Intelligence in Medicine
• Ophthalmology Research
• Clinical AI Applications

Background:

• Foundation models represent a significant advancement in artificial intelligence (AI), moving beyond specialized tasks to generalizable applications.
• Ophthalmology is poised to lead other medical fields in adopting these versatile AI models into clinical practice.
• Understanding and integrating AI foundation models is crucial for the future of eyecare.

Purpose of the Study:

• To provide a roadmap for eyecare professionals on understanding AI foundation models.
• To equip readers with knowledge for exploring foundation models in their research and practice.
• To review the current state and future potential of foundation models in ophthalmology.

Main Methods:

• Outlining key concepts and technological advancements enabling foundation models.
• Reviewing novel training approaches and modern AI architectures.
• Summarizing existing literature on foundation models in ophthalmology, including vision, language, and multimodal models.

Main Results:

• Foundation models offer broad applicability across diverse ophthalmology subfields.
• Progress has been made in vision foundation models, large language models, and large multimodal models.
• Existing literature highlights the transformative potential of these AI models in eyecare.

Conclusions:

• AI foundation models present a paradigm shift with vast potential in ophthalmology.
• Key challenges include data privacy, algorithmic bias, and rigorous clinical validation.
• Strategic steps are necessary to maximize the benefits of foundation models in clinical practice.