Page 27 - IMDR Journal 2025

Research Article
for Type 2 Diabetes Mellitus (T2DM), this study uses a and glucose metabolism. By offering specialized therapies
secondary research approach, using published literature, that maximize patient outcomes, AI-driven healthcare
medical reports, and AI case studies. Peer-reviewed articles, solutions close this gap.
government health reports, business case studies, and AI-
AI-Powered Insulin Dosage Adjustment
powered diabetes management solutions are the sources of
To suggest accurate insulin dosages, machine learning
the data. The study examines AI's function in clinical
decision support, remote monitoring, individualized models examine food consumption, physical activity, and
treatment, and early diagnosis by combining qualitative and glucose variations. In order to prevent hypoglycemia and
quantitative findings. To evaluate advances in glycemic hyperglycemia, AI-powered automated insulin
control, complication prevention, and healthcare cost administration systems, such the Medtronic MiniMed 670G
reduction, a comparison between AI-driven predictive hybrid closed-loop system, modify insulin infusion rates in
models and traditional diabetes care is made. This study real time. AI-assisted insulin pumps anticipate post-meal
also looks at legal restrictions, data security issues, and glucose increases and adjust insulin delivery based on data
ethical dilemmas surrounding the use of AI in healthcare. from continuous glucose monitors (CGMs).
The results are intended to offer practical suggestions for AI-Powered Medication and Lifestyle Plans
improving AI-based diabetic care. Real-time food consumption data and glucose levels are
AI in Predictive Healthcare for Type 2 Diabetes used by AI-based nutritional advice systems, such as
Neutrino, to customize meal plans. AI-powered fitness
Early Detection Systems Driven by AI
trackers ensure optimal blood sugar regulation by
Preventing the growth and effects of Type 2 diabetes and
suggesting workout regimens based on a patient's weight,
prediabetes requires early identification. Traditional insulin sensitivity, and activity history. AI improves
diagnostic approaches rely on periodic blood tests and risk adherence and lowers adverse drug responses by modifying
factor assessments, which may fail to detect high-risk prescription schedules in response to a patient's response to
individuals in time. By using pattern recognition and risk therapy.
assessment algorithms, AI-driven predictive models
provide a more proactive and data-driven approach, AI-Powered Remote Surveillance
improving early diagnosis. AI-powered remote monitoring tools allow for ongoing
health surveillance and offer real-time information on
Recognizing People at High Risk
insulin use, blood sugar levels, and lifestyle choices. These
To determine who is at risk of Type 2 diabetes, AI models use developments give patients the means for early intervention
genetic predisposition, lifestyle factors, past glucose and self-management while lessening the strain on medical
readings, and electronic health records (EHRs). In order to facilities.
give patients and healthcare professionals early warnings,
machine learning (ML) algorithms evaluate age, BMI, AI Wearable Technology for Constant Monitoring
blood pressure, cholesterol, and eating habits using Continuous glucose monitors (CGMs) and smart insulin
predictive analytics. Early identification of diabetic pens offer insights into insulin dosing and real-time glucose
retinopathy has been made possible by the use of deep tracking. Artificial intelligence (AI) algorithms are used by
learning-based image processing in AI-powered eye scans devices like the Abbott Freestyle Libre and Dexcom G6 to
to identify retinal abnormalities associated with diabetes. forecast blood sugar patterns and issue warnings for
possible episodes of hypoglycemia or hyperglycemia. In
Models of Risk Assessment Driven by AI
order to determine the factors impacting blood sugar swings,
Neural networks, decision trees, and support vector AI-powered biosensors and smartwatches (such as the Fitbit
machines (SVMs) have demonstrated great accuracy in and Apple Watch) track heart rate variability, activity levels,
anticipating the beginning of diabetes years before clinical
and stress patterns.
symptoms manifest. Large-scale healthcare datasets have
Self-Management Mobile Apps Driven by AI
been used to train AI models like Google's DeepMind,
which improve risk stratification by predicting problems AI is used by MySugr, Livongo, and BlueLoop to offer
associated to diabetes. By using AI to assess patient tailored feedback on dietary decisions, exercise regimens,
biomarkers and identify the phases of prediabetes, IBM and glucose trends. AI-powered chatbots and virtual
Watson Health helps stop the development of diabetes. assistants help patients by responding to their questions,
Healthcare professionals can reduce the prevalence of Type reminding them to take their prescriptions, and deriving
2 diabetes by implementing focused preventative insights from health data. Patients can monitor their health
interventions, recommending lifestyle changes, and holistically without frequent hospital visits thanks to certain
intervening earlier when AI is integrated into early detection apps that link with fitness trackers and CGMs.
systems.
Tailored Therapy Programs Medical Decision Support Systems
By providing individualized treatment recommendations Using AI to Make Clinical Decisions
based on real-time patient data, artificial intelligence is
revolutionizing the management of diabetes. Conventional AI-driven decision support systems help physicians
diabetes care adheres to broad recommendations, frequently diagnose diabetes and anticipate complications by
ignoring individual differences in comorbidities, lifestyle,

18