The role of AI and machine learning in optimizing insulin therapy: a comparative study

Managing diabetes effectively requires precise insulin dosing. AI and ML have emerged as valuable tools in optimizing insulin therapy. This study compares AI/ML-based insulin optimization with standard therapy to assess its impact on glycemic control and patient satisfaction. A quasi-experimental study was conducted involving 100 patients divided into AI-assisted and standard insulin therapy groups. Primary outcomes measured included HbA1c levels and frequency of hypoglycemic episodes, while secondary outcomes included patient satisfaction and adherence rates. Statistical tests such as paired t-tests, chi-square tests, and ANOVA were applied. Patients in the AI-assisted therapy group exhibited a significant reduction in HbA1c levels (p<0.05), fewer hypoglycemic episodes (p<0.05), and higher satisfaction levels (p<0.05) compared to the standard therapy group. AI and ML-based insulin optimization improve glycemic control, reduce hypoglycemia, and enhance patient satisfaction, making it a valuable addition to diabetes management strategies.