Using ChatGPT-4 in visual field test assessment

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.

This study aims to evaluate the capability of ChatGPT-4 in interpreting visual field tests.

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.

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.

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.