International Journal of Community Medicine and Public Health | April 2025 | Vol 12 | Issue 4 Page 1873 International Journal of Community Medicine and Public Health Reddy SD. Int J Community Med Public Health. 2025 Apr;12(4):1873-1876 http://www.ijcmph.com pISSN 2394-6032 | eISSN 2394-6040 Short Communication The role of AI and machine learning in optimizing insulin therapy: a comparative study Sagam Dinesh Reddy* INTRODUCTION Diabetes mellitus is a chronic metabolic disorder requiring meticulous glycemic control. AI and ML have transformed diabetes management by enabling personalized, real-time insulin dosing adjustments. However, research comparing AI-based insulin optimization with conventional physician-guided insulin therapy remains limited. This study evaluates AI/ML- based insulin dosing’s impact on glycemic control and patient outcomes. 1-3 METHODS Study design This was a Quasi-experimental study. Study location The study was conducted in LMR Hospital, Andhra Pradesh. Study duration The study duration was of 1 year from January 2023- December 2023. Samples size The sample size taken for study was of 100 patients (50 per group). Sampling technique Technique chosen for sampling was purposive sampling. ABSTRACT 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. Keywords: Artificial intelligence, Machine learning, Insulin optimization, Diabetes care, Glycemic control, Hypoglycemia Department of Family Medicine/Industrial Health, LMR Hospital, G Konduru, Andhra Pradesh, India Received: 07 December 2024 Revised: 18 February 2025 Accepted: 19 February 2025 *Correspondence: Dr. Sagam Dinesh Reddy, E-mail: dineshsagam143@gmail.com Copyright: © the author(s), publisher and licensee Medip Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. DOI: https://dx.doi.org/10.18203/2394-6040.ijcmph20250938