Aims <p>This proof-of-concept study aims to demonstrate the feasibility of using AI-Simulated Patients (ASPs) to test and illustrate the tailored selection of diabetes remission prediction models.</p> Methods <p>We created five initial ASPs representing diverse T2DM cases, each with key clinical parameters, then expanded to 100 ASPs as a convenience sample. Using a standardized process, GPT-4 applied six validated diabetes remission prediction models: DiaRem, A-DiaRem, ABCD, IMS, DiaBetter, and DRI. For each case, GPT-4 assessed input availability, estimated remission scores, discussed model strengths/limitations, and recommended the best-fit tool. Human experts supervised all outputs to ensure accuracy, reliability, and appropriate model selection for each ASP. Exact ranges and distributions were anchored in reported epidemiology of bariatric patient populations (e.g., age 18–70 years, BMI 30–65&#xa0;kg/m², HbA1c 6–11%, C-peptide 0.2–5 ng/mL) with variability introduced for diversity. Sample size (<i>n</i> = 100) was chosen for breadth of illustration rather than statistical power.</p> Results <p>We applied a refined, criteria-based algorithm to 100 ASPs representing diverse T2DM cases considered for metabolic/bariatric surgery. Model selection incorporated key inputs, planned procedure, and model validation scope. The DRI was most common, followed by DiaBetter, ABCD Score, Advanced DiaRem, and IMS Score. Each recommendation reflected surgical type and data completeness. The resulting decision-making algorithm illustrates a reproducible methodology that may support future tailored application of remission prediction models, pending validation in real clinical datasets.</p> Conclusions <p>This study demonstrates that LLM-generated simulated patients can refine a reproducible, transparent, and ethically safe algorithm for selecting diabetes remission prediction models, with future validation required using real-world clinical data.</p>