A User-Friendly Approach for the Diagnosis of Diabetic Retinopathy Using ChatGPT and Automated Machine Learning

Purpose To assess the capabilities of ChatGPT and Vertex AI in executing code-free preprocessing, training machine learning models, and analyzing the data. Design Evaluation of diagnostic test or technology. Participants ChatGPT and Vetrex AI as publicly available LLM and machine learning platform, respectively. Methods ChatGPT was employed to improve the resolution of fundus photography images from the MESSIDOR-2 open-source dataset using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique by FIJI software. Subsequently, Vertex AI, an automated machine learning (AutoML) platform, was utilized to develop two classification models. The first model served as a binary classifier for detecting the presence of diabetic retinopathy (DR), while the second determined its severity. Finally, ChatGPT was used to provide scripts for R and Python programming languages for data analysis and was also directly employed in analyzing the data in a code-free method. Main Outcome Measures Evaluating the utility of ChatGPT in generating scripts for preprocessing images using Fiji and analyzing data across Python, and R and assessing its potential in analyzing data through a code-free method. Investigating the capabilities of Vertex AI to train image classification models for detection of DR and its severity. Results Two machine learning models were trained using 1740 images from the MESSIDOR-2 database. The first model, designed to detect the severity of diabetic retinopathy, achieved an area under the precision-recall curve (AUPRC) of 0.81, with a precision rate of 81.81% and recall of 72.83%. The second model, tailored for the detection of the presence of DR, recorded a precision and recall of 84.48% with an AUPRC of 0.90. Conclusion ChatGPT and Vertex AI have the potential to enable physicians without coding expertise to preprocess images, analyze data, and train machine learning models.