Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization.

Area of Science:

• Ophthalmology
• Artificial Intelligence
• Medical Informatics

Background:

• The integration of artificial intelligence (AI) in ophthalmology offers significant potential for advancing patient care.
• Historically, the development and deployment of AI in medicine have been hindered by resource limitations and technical expertise requirements.

Purpose of the Study:

• To review the current landscape of resources that facilitate the democratization of AI in ophthalmology.
• To identify tools and platforms that enable clinicians to independently drive AI initiatives.

Main Methods:

• Literature review of existing resources for AI development and deployment.
• Analysis of tools enabling AI accessibility for non-specialists.

Main Results:

• Key resources include open datasets, efficient data labeling techniques, and code-free automated machine learning (AutoML) platforms.
• Cloud-based platforms facilitate the deployment of AI models, making them accessible to clinicians with limited resources.
• These tools empower clinicians to initiate and manage their own AI projects.

Conclusions:

• Clinicians, as domain experts, are ideally positioned to lead AI projects focused on patient-relevant outcomes.
• The availability of open datasets, efficient labeling, code-free AutoML, and cloud platforms removes significant barriers to clinician-driven AI development.
• The increasing democratization of AI through these tools promises substantial benefits for both clinicians and patients in ophthalmology.