Abstract
BACKGROUND: Oculomics is an emerging medical field that focuses on the study of the eye to detect and understand systemic diseases. ChatGPT-4 is a highly advanced AI model with multimodal capabilities, allowing it to process text and statistical data. Osteoporosis is a chronic condition presenting asymptomatically but leading to fractures if untreated. Current diagnostic methods like dual X-ray absorptiometry (DXA) are costly and involve radiation exposure. This study aims to develop a cost-effective osteoporosis risk prediction tool using ophthalmological data and ChatGPT-4 based on oculomics, aligning with predictive, preventive, and personalized medicine (3PM) principles. WORKING HYPOTHESIS AND METHODS: We hypothesize that leveraging ophthalmological data (oculomics) combined with AI-driven regression models developed by ChatGPT-4 can significantly improve the predictive accuracy for osteoporosis risk. This integration will facilitate earlier detection, enable more effective preventive strategies, and support personalized treatment plans tailored to individual patients. We utilized DXA and ophthalmological data from the Korea National Health and Nutrition Examination Survey to develop and validate osteopenia and osteoporosis prediction models. Ophthalmological and demographic data were integrated into logistic regression analyses, facilitated by ChatGPT-4, to create prediction formulas. These models were then converted into calculator software through automated coding by ChatGPT-4. RESULTS: ChatGPT-4 automatically developed prediction models based on key predictors of osteoporosis and osteopenia included age, gender, weight, and specific ophthalmological conditions such as cataracts and early age-related macular degeneration, and successfully implemented a risk calculator tool. The oculomics-based models outperformed traditional methods, with area under the curve of the receiver operating characteristic values of 0.785 for osteopenia and 0.866 for osteoporosis in the validation set. The calculator demonstrated high sensitivity and specificity, providing a reliable tool for early osteoporosis screening. CONCLUSIONS AND EXPERT RECOMMENDATIONS IN THE FRAMEWORK OF 3PM: This study illustrates the value of integrating ophthalmological data into multi-level diagnostics for osteoporosis, significantly improving the accuracy of health risk assessment and the identification of at-risk individuals. Aligned with the principles of 3PM, this approach fosters earlier detection and enables the development of individualized patient profiles, facilitating personalized and targeted treatment strategies. This study also highlights the potential of AI, specifically ChatGPT-4, in developing accessible, cost-effective, and radiation-free screening tools for advancing 3PM in clinical practice. Our findings emphasize the importance of a holistic approach, incorporating comprehensive health indices and interdisciplinary collaboration, to deliver personalized management plans. Preventive strategies should focus on lifestyle modifications and targeted interventions to enhance bone health, thereby preventing the progression of osteoporosis and contributing to overall patient well-being. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-024-00378-0.