Saturday, November 7, 2026

11:45 AM - 12:00 PM

Ballroom 1 and 2

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Overview

Dr Michelle Courtney-Harris

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Details

Purpose Despite the high prevalence of ocular presentations in general practice and emergency departments, dedicated ophthalmology teaching time has declined worldwide. This study evaluates EyeBot, an AI-based patient simulator, assessing its validity for ophthalmic clinical simulation and effectiveness in supporting ophthalmic education and patient interaction across a range of learner groups including medical staff and orthoptists. Method A mixed-methods design was used to investigate the educational value of EyeBot by examining its clinical accuracy, effectiveness, user-experience and relevance. Healthcare professionals (N=36) participated in the study engaging with EyeBot through three simulated ophthalmic emergency scenarios delivered on a customized ChatGTP platform. During each scenario participants obtained a history, requested investigations and formulated a diagnosis and management plan. EyeBot delivered performance-based feedback. Participants then completed questionnaires and interviews to evaluate their experience. Results EyeBot showed good potential as a valid and authentic clinical simulation tool, receiving favourable ratings for realism of the clinical scenarios. Patient authenticity ratings were less positive, some participants noting the simulated patient interactions did not always reflect natural patient behaviour. Feedback generated by EyeBot was well received, participants reported it provided useful guidance and constructive recommendations aligned with safe clinical practice. However, the system was less effective in identifying and interpreting errors, particularly when detecting omitted examinations and distinguishing between minor mistakes and clinically significant errors. Most participants (86.5%) indicated they were comfortable using EyeBot for learning, considering it as particularly valuable for self-directed learning, exam revision and readiness for clinical placement. Furthermore, 93.8% agreed that AI could support their clinical reasoning and diagnostic decision making, reinforcing EyeBot's role in ocular healthcare education. Conclusion EyeBot is a promising education platform for clinical vision health training, providing an accessible and engaging environment in which learners can develop clinical knowledge and decision-making skills. Future phases of this research will focus on evaluating education impact on students and exploring additional applications

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Speaker