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Background: Type 2 diabetes (T2D) is a prevalent chronic disease with asignificant risk of serious health complications and negative impacts on thequality of life. Given the impact of individual characteristics and lifestyleon the treatment plan and patient outcomes, it is crucial to develop preciseand personalized management strategies. Artificial intelligence (AI) providesgreat promise in combining patterns from various data sources with nurses'expertise to achieve optimal care. Methods: This is a 6-month ancillary studyamong T2D patients (n = 20, age = 57 +- 10). Participants were randomlyassigned to an intervention (AI, n=10) group to receive daily AI-generatedindividualized feedback or a control group without receiving the daily feedback(non-AI, n=10) in the last three months. The study developed an onlinenurse-in-the-loop predictive control (ONLC) model that utilizes a predictivedigital twin (PDT). The PDT was developed using a transfer-learning-basedArtificial Neural Network. The PDT was trained on participants self-monitoringdata (weight, food logs, physical activity, glucose) from the first threemonths, and the online control algorithm applied particle swarm optimization toidentify impactful behavioral changes for maintaining the patient's glucose andweight levels for the next three months. The ONLC provided the interventiongroup with individualized feedback and recommendations via text messages. ThePDT was re-trained weekly to improve its performance. Findings: The trainedONLC model achieved >=80% prediction accuracy across all patients while themodel was tuned online. Participants in the intervention group exhibited atrend of improved daily steps and stable or improved total caloric and totalcarb intake as recommended.

Nurse-in-the-Loop Artificial Intelligence for Precision Management of  Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive  Digital Twin