Assessment of ChatGPT in the preclinical management of ophthalmological emergencies – an analysis of ten fictional case vignettes

Abstract

Background/Aims: The artificial intelligence (AI) based platform ChatGPT (Chat Generative Pre-Trained Transformer, OpenAI LP, San Francisco, CA, USA) has gained an impressing popularity over the past months. Its performance on case vignettes of general medical (non-ophthalmological) emergencies has priorly been assessed with very encouraging results. The purpose of this study is to assess the performance of ChatGPT on ophthalmological emergency case vignettes in terms of the main outcome measures triage accuracy, appropriateness of recommended preclinical measures and overall potential to inflict harm to the user/patient. Methods: We wrote ten short, fictional case vignettes describing different acute ophthalmological symptoms. Each vignette was entered into ChatGPT five times with the same wording and following a standardized interaction pathway. The answers were analysed in a standardised manner. Results: We observed a triage accuracy of 87.2%. Most answers contained only appropriate recommendations for preclinical measures. However, an overall potential to inflict harm to users/patients was present in 32% of answers. Conclusion: ChatGPT should not be used as a stand-alone primary source of information about acute ophthalmological symptoms. As AI continues to evolve, its safety and efficacy in the preclinical management of ophthalmological emergencies has to be reassessed regularly.