Coherent Interpretation of Entire Visual Field Test Reports Using a Multimodal Large Language Model (ChatGPT)

Abstract

This study assesses the accuracy and consistency of a commercially available large language model (LLM) in extracting and interpreting sensitivity and reliability data from entire visual field (VF) test reports for the evaluation of glaucomatous defects. Single-page anonymised VF test reports from 60 eyes of 60 subjects were analysed by an LLM (ChatGPT 4o) across four domains-test reliability, defect type, defect severity and overall diagnosis. The main outcome measures were accuracy of data extraction, interpretation of glaucomatous field defects and diagnostic classification. The LLM displayed 100% accuracy in the extraction of global sensitivity and reliability metrics and in classifying test reliability. It also demonstrated high accuracy (96.7%) in diagnosing whether the VF defect was consistent with a healthy, suspect or glaucomatous eye. The accuracy in correctly defining the type of defect was moderate (73.3%), which only partially improved when provided with a more defined region of interest. The causes of incorrect defect type were mostly attributed to the wrong location, particularly confusing the superior and inferior hemifields. Numerical/text-based data extraction and interpretation was overall notably superior to image-based interpretation of VF defects. This study demonstrates the potential and also limitations of multimodal LLMs in processing multimodal medical investigation data such as VF reports.

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