5 Key Takeaways

• 1

  AI in eye-brain assessment identifies patterns but does not directly diagnose pathologies, relying on quality training data.

• 2

  Video-oculography quantifies metrics like saccadic latency and fixation stability, aiding in screening and prognosis but is not a standalone diagnostic tool.

• 3

  Clinical validation and real-world performance must align with intended use before adopting AI tools in ophthalmology.

• 4

  AI complements clinicians by providing precise measurements, while human expertise remains essential for interpretation and patient management.

• 5

  Challenges in integrating AI include workflow disruptions, data silos, and unclear reimbursement pathways, impacting adoption in clinical settings.