The September 2024 Eversight Academy webinar featured Travis Redd, MD, MPH, Assistant Professor of Ophthalmology at Oregon Health and Science University. As both a cornea specialist and someone whose research explores the application of emerging technologies to reduce the burden of corneal blindness domestically and abroad, Dr. Redd provided valuable insights into the current state and future potential of artificial intelligence in corneal care.
Understanding AI in medicine
Current applications in corneal care
Several promising areas of AI implementation were discussed, including:
- Infectious keratitis - AI models now performing on par with expert cornea specialists in distinguishing bacterial from fungal infections
- Keratoconus - Excellent performance in detecting manifest cases, with current limitations in subclinical detection
- Corneal transplantation - Successful detection of graft detachment and prediction of rebubbling needs
- Trachoma screening - Near-perfect performance in identifying inflammation, with potential for community-level screening programs
Implementation approaches
Dr. Redd outlined three main paradigms for AI implementation:
- Assistive - Supporting clinical decision-making with quantifiable measurements
- Augmentative - Working alongside clinicians to provide additional insights
- Autonomous - Independent screening and triage, similar to existing diabetic retinopathy systems
Barriers to clinical implementation
Key challenges include:
- The "black box" nature of AI decision-making
- Variable image quality in real-world settings
- Need for more diverse and representative datasets
- Unclear reimbursement mechanisms
- Limitations of supervised learning in medical contexts
Global health impact
During Q&A, Dr. Redd highlighted the potential transformative impact in lower and middle-income countries, particularly for:
- Infectious keratitis management
- Trachoma screening and treatment planning
- Community-level disease detection
Building trust
Dr. Redd emphasized several key factors for building trust in AI systems:
- Better communication of research results to non-technical audiences
- Testing with diverse, representative datasets
- Local validation of AI tools in target communities
- Transparent reporting of model performance
Future directions
To move AI from research to clinical practice, focus areas include:
- Development of more explainable AI models
- Creation of diverse data repositories
- Standardization of imaging data formats
- Implementation of federated learning approaches
- Greater involvement of clinicians in AI development
Dr. Redd concluded by encouraging interdisciplinary collaboration between clinicians, machine learning engineers, public health experts and patient advocates to bridge the gap between published research and practical implementation.
Eversight's free webinars are a great way for you to connect, learn and train digitally with leading ophthalmologists and researchers from around the world. We invite you to RSVP for scheduled webinars and browse our recording library.
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About the author
Michael Szkarlat, Partner Development Director
Michael has been with Eversight since 2016 and has recently worked to develop Eversight's educational wet lab programs for EK surgery and a standardized protocol for DALK practice in a wet lab setting. His eye banking experience is rooted in the preparation of corneal grafts and spent nearly five years as Eversight’s Medical Director designee in charge of training clinical team members to prepare corneal tissue for DMEK and DSAEK surgery. In his time at Eversight, Michael has presented at scientific conferences, been involved in clinical research and developed innovations in tissue processing. He was named an IAPB Eye Heath Hero in the innovations category. Michael is passionate about community-based eye banking and honoring the precious gift that is donation. When not at work, he enjoys traveling with his wife and baking artisan sourdough bread.
