Using ChatGPT-4 in visual field test assessment

Abstract

CLINICAL RELEVANCE: Visual field testing is essential in the diagnosis and management of various ophthalmic diseases, particularly glaucoma. Integrating ChatGPT-4 into the interpretation of these tests has the potential to aid clinical decision making and improve efficiency and accessibility in clinical practice. BACKGROUND: This study aims to evaluate the capability of ChatGPT-4 in interpreting visual field tests. METHOD: A total of 30 patient visual field printouts, either with or without defects, were included in this study. The performance of ChatGPT-4 in identifying test name, pattern, reliability indices, total deviation map, pattern deviation map and greyscale map was evaluated and compared with that of 2 experienced glaucoma consultants. The study also focused on the ability of ChatGPT to categorise tests as 'normal' or suggest diagnosis by interpreting tests accurately. RESULTS: The results showed that ChatGPT-4 was highly accurate in identifying test names (100%), patterns (90%) and global visual field indices (96.7%). It also accurately classified tests as reliable or unreliable (93.3%).The model provided 66.7% and 30% accurate and adequate answers in interpreting deviation and greyscale maps, respectively. In addition, in 33.3% of tests, it was able to accurately interpret the visual field test and classify it as 'normal' or suggest a diagnosis. CONCLUSION: The study highlights the potential of large language models like ChatGPT-4 in assessing visual field tests. ChatGPT-4 could interpret numeric data on tests accurately. However, it was inadequate in interpreting deviation and greyscale maps and suggesting a diagnosis according to the defects.

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