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Diagnosing Glaucoma Based on the Ocular Hypertension Treatment Study Dataset Using Chat Generative Pre-Trained

Hina Raja^1,2, Xiaoqin Huang¹, Mohammad Delsoz¹

¹Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, Tennessee.

Ophthalmology Science

|September 30, 2024

Summary

This summary is machine-generated.

This study assessed large language models for glaucoma diagnosis using the Ocular Hypertension Treatment Study dataset. ChatGPT 4.0 demonstrated higher accuracy than ChatGPT 3.5, showing promise for clinical applications.

Keywords:

ChatGPT Diagnosis Glaucoma Large language models

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Published on: August 14, 2013

Area of Science:

Ophthalmology
Artificial Intelligence
Medical Diagnostics

Background:

Glaucoma diagnosis relies on comprehensive patient data.
Large language models (LLMs) offer potential for analyzing complex medical datasets.
Evaluating LLM diagnostic capabilities is crucial for their clinical integration.

Purpose of the Study:

To assess the diagnostic performance of Chat Generative Pre-Trained Transformer (ChatGPT) in identifying glaucoma.
To compare the diagnostic accuracy of ChatGPT 3.5 and ChatGPT 4.0 using the Ocular Hypertension Treatment Study (OHTS) dataset.
To explore the utility of LLMs as a tool in glaucoma diagnostics.

Main Methods:

Utilized the Ocular Hypertension Treatment Study (OHTS) dataset, including 3170 eyes from 1585 subjects.
Converted tabular patient data (demographics, clinical, ocular, visual field, optic nerve head photos, disease history) into textual case reports.
Employed the ChatGPT API to query versions 3.5 and 4.0 for glaucoma diagnoses based on the case reports.

Main Results:

ChatGPT 3.5 achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.74, 66% accuracy, 64% specificity, 85% sensitivity, and a 0.72 F1 score.
ChatGPT 4.0 achieved an AUC of 0.76, 87% accuracy, 90% specificity, 61% sensitivity, and a 0.92 F1 score.
ChatGPT 4.0 exhibited superior overall accuracy and specificity compared to ChatGPT 3.5.

Conclusions:

ChatGPT 4.0 shows promising accuracy for glaucoma diagnosis from structured data, outperforming ChatGPT 3.5.
ChatGPT 3.5 demonstrated higher sensitivity, while ChatGPT 4.0 excelled in specificity.
LLMs like ChatGPT can be valuable tools for analyzing ocular hypertensive patient data, with future multimodal versions potentially enhancing diagnostic accuracy further.