Codeless Development of a Customized SMILE Nomogram Using a Large Language Model: A Practical Framework for Clinicians

<b>Purpose:</b> To evaluate the feasibility of using ChatGPT-4, a large language model (LLM), to develop a customized nomogram calculator for small-incision lenticule extraction (SMILE) surgery based on institution-specific data, without requiring any coding expertise. Customized nomograms are essential due to variations in surgical practices, patient populations, and diagnostic equipment across vision correction centers. <b>Methods:</b> A retrospective analysis of consecutive patients was performed on data of 1268 eyes that underwent SMILE. Preoperative measurements and postoperative refractive errors at 6 months were collected and analyzed. The entire dataset was divided into a training set and validation set at a ratio of 3:1. After data anonymization, ChatGPT-4 was instructed to perform a linear regression analysis to predict postoperative refractive errors using preoperative data. Subsequently, we instructed ChatGPT-4 to generate HTML code for a webpage-based nomogram calculator that inputs preoperative data and calculates surgical parameters using the derived formulas. The results of the regression analysis performed using ChatGPT-4 were compared with those obtained using two conventional statistical software programs, R and SPSS. <b>Results:</b> ChatGPT-4 successfully performed SMILE nomogram regression analysis. The predicted SMILE parameters were not significantly different from those obtained using the statistical software. The nomogram showed a higher predictive ability for postoperative refractive error than the simple empirical nomogram (<i>p</i> < 0.001). We successfully created a webpage-based calculator using ChatGPT-4 through multiple prompt instructions without coding. <b>Conclusion:</b> ChatGPT-4 not only provides a statistical model for SMILE nomograms but also creates a calculator for user convenience. Clinicians can easily build their own nomogram calculators using only the collected data without coding. The advanced LLM will allow clinicians to conveniently create customized nomogram tools.