Artificial intelligence-driven patient history and symptoms combined with slit-lamp eye simulation for enhancing the clinical training of students

Abstract

CLINICAL RELEVANCE: Communication between eye care practitioners is essential to optimise health care. Traditionally, actors have been used prior to real patient exposure, but this is expensive and much training is needed to ensure a consistent student experience and assessment. Artificial intelligence using ChatBots is shown to provide a high-quality student experience, and due to their portability and cost, it has the potential to revolutionise communication training. BACKGROUND: Augmented reality ocular examination simulations have been shown to be effective in teaching ophthalmology and optometry. With large language models, ChatGPT has been shown to provide effective role-play simulation. This study examined whether a combination of augmented reality and role-play simulation can enhance self-assessed competency of optometry students. It also assessed, the learning experience of students with role play by a human actor compared to different artificial intelligence chatbots. METHOD: Sixteen final-year optometry students with limited experience of computer simulation completed three role-plays with each of a SimConverse artificial intelligence, ChatGPT artificial intelligence, and an actor patient, in randomised order. In each session, the scenario included a history and symptoms, related augmented reality slit-lamp biomicroscopy examination (EyeSi) of the 'patient's' eyes, followed by the student communicating their findings and intended actions with the 'patient'. Students completed pre- and post-questionnaires to rank their learning experiences. RESULTS: < 0.001). CONCLUSION: Combining patient role-play with augmented reality simulation significantly enhances how students feel about their clinical competencies. Role-play by an artificial intelligence 'patient' can provide an equivalent learning experience as that provided by an actor.

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