Individualized Therapy Optimization for Type 2 Diabetes: Retrospective Study of Model Development, Internal Validation, and Physician Evaluation

Abstract Background Type 2 diabetes is a widespread chronic condition in which blood glucose and body weight management constitute essential therapeutic targets. Emerging technologies have the potential to aid complex therapeutic pharmacotherapy choices that are optimally tailored to individual needs. Objective In this study, we developed and evaluated an AI model combining guidelines with clinical features and continuous glucose monitoring (CGM) to optimize therapeutic decision-making. Methods Therapeutic guidelines were first encoded using a rule-based model and trained on a feed-forward neural network to predict the probability of therapeutic success for individual treatment recommendations. This approach relied on real-world evidence from a specialist diabetes outpatient clinic, using historical clinical data generated between 2009 and 2023. We used data from 533 patients with a diagnosis of type 2 diabetes and complete baseline data for weight and hemoglobin A 1c within relevant therapy windows, resulting in a total of 853 treatment regimens. Transfer learning was used to optimize for glucose-lowering therapies that led to successful treatment outcomes, defined as an absolute 0.3% reduction in hemoglobin A 1c (when it is over 6.5%) without weight gain in patients with a BMI over 28 kg/m 2 . Recommendations that deviated from the guidelines were described using Shapley values and tested in digital twins for statistical significance. Four CGM-derived glucose-insulin response dynamic factors served as additional biomarkers. Results Dual glycemic and weight targets were achieved in actual clinical practice in 51.2% (131/256) of cases, increasing to 54% (20/37) when clinical guidelines were followed. After selecting outcomes in the test set that followed individualized recommendations, this increased further to 58% (21/36) when using only phenotypic markers and to 65% (22/34) when adding CGM-derived dynamic factors. Conclusions Tested on the limited number of patients available, our findings show that our AI model was associated with improved retrospective outcomes compared to the guidelines in complex type 2 diabetes cases by integrating multiple data sources, drawing on experiential clinical insights, and selecting treatments most likely to meet each patient’s clinical targets for glucose and weight control. Future research is needed with a larger dataset.