Page 28 - IMDR Journal 2025

Research Article
combining imaging results, lab reports, and patient data. For patient's unique reaction. In order to balance blood glucose
better patient outcomes, these tools assist doctors in levels, it modifies insulin dosage recommendations, makes
identifying high-risk patients, optimizing medication recommendations for the best times to eat, and customizes
dosages, and creating individualized treatment regimens. exercise regimens. Livongo and MySugr, two AI-powered
Early intervention is made possible by AI models such as diabetes care apps, offer real-time information, warning
DeepMind's Streams App, which assist in identifying users of possible hyperglycemia episodes and assisting with
symptoms of acute kidney damage, a major consequence of medication and food choices.
diabetes. Reduction in Diabetes-Related Complications
Interpreting Lab Reports with AI Assistance Uncontrolled diabetes significantly raises the risk of long-
By spotting hidden patterns and irregularities in test data, term effects, including diabetic retinopathy, neuropathy,
artificial intelligence improves the accuracy of blood nephropathy, and cardiovascular diseases. AI-driven
glucose testing. AI algorithms analyse cardiovascular healthcare models enable the early detection and prevention
parameters, retinal scans, and photos of foot ulcers to find of these issues, which improves patient outcomes and
early indicators of problems from diabetes. EHR systems reduces hospitalization rates.
with AI capabilities, such as Epic Systems and Cerner, AI in Early Detection of Complications
evaluate test results, drug efficacy, and patient history to help
physicians make well-informed decisions. AI-powered image recognition algorithms analyse retinal
scans to find early signs of diabetic retinopathy with an
accuracy rate of over 90%. Google's DeepMind AI and IDx-
Effectiveness of AI-Based Predictive Healthcare Models DR, an FDA-approved AI for retinal screening, have both
Glycemic control, complication prevention, and cost been successful in identifying retinal degeneration in
reduction have all been shown to be significantly improved diabetic patients before symptoms manifest. Wearable
by the use of AI-driven predictive models in diabetes care. artificial intelligence (AI) devices monitor heart rate
Conventional diabetic treatment is frequently reactive, variability, blood pressure, and oxygen saturation to predict
cardiovascular risks in individuals with diabetes.
dealing with issues only after they occur. However, AI
makes it possible to take a proactive stance, improving AI-Powered Preventive Actions
patient outcomes through ongoing monitoring, predictive AI reduces the risk of kidney damage and the advancement
analytics, and tailored treatment modifications. This section of cardiovascular disease by offering tailored advice on
examines the ways in which AI improves glycemic control, dietary, exercise, and medication changes. AI-based risk
lowers complications associated with diabetes, and stratification algorithms divide diabetes patients into high-
increases healthcare systems' cost-effectiveness. risk, moderate-risk, and low-risk groups, helping doctors
Artificial Intelligence-Enhanced Continuous Glucose prioritize patient care. AI-driven telemedicine technologies
Monitoring (CGM) facilitate early intervention and remote monitoring, halting
Conventional glucose monitoring uses fingerstick tests, the progression of illness problems.
which only give momentary readings of blood sugar levels. Lower Mortality and Hospitalization Rates
AI-powered CGMs, on the other hand, provide continuous AI uses patient-reported symptoms, medication adherence,
and real-time glucose monitoring, enabling immediate and blood glucose patterns to forecast hospital readmission
nutritional and insulin modifications. Machine learning risks. AI-powered automated alerts enable prompt medical
algorithms are used by devices such as Abbott Freestyle intervention by informing healthcare practitioners of
Libre and Dexcom G6 to monitor glucose patterns and significant glucose variations. Clinical research shows that
forecast hypoglycemic (low blood sugar) and improved preventive treatment and early problem detection
hyperglycemic (high blood sugar) occurrences. Glycemic
from AI-assisted diabetes management can lower
variability is decreased using AI-based CGM systems,
hospitalization rates by as much as 30%.
which results in less severe glucose fluctuations and lower
HbA1c levels. The Economic Viability of AI-Powered Diabetes
Treatment
Artificial Intelligence-Based Blood Sugar Predictive
Models Diabetes has a significant financial impact on healthcare
systems; the yearly cost of treatment exceeds $966 billion
By examining past glucose data, eating habits, physical (IDF, 2021). By reducing hospital stays, allocating
activity, and stress levels, AI predicts blood sugar swings resources optimally, and averting costly consequences, AI-
before they happen. In order to reduce night-time based healthcare solutions can cut costs. Lowering
hypoglycemia and post-meal spikes, reinforcement learning Emergency Care Expenses and Hospital Visits
algorithms dynamically modify insulin dosages. Time-in-
range (TIR) glucose levels are improved by AI-based Patients can control their diabetes from home with AI-
automated insulin delivery (AID) systems, according to powered self-management tools and remote monitoring,
studies, which guarantees better long-term diabetes which eliminates the need for frequent hospital stays.
treatment. Predictive analytics reduces hospitalization costs by
preventing emergency complications. Virtual consultations
Customized Glycemic Management with AI are made possible by AI-powered telemedicine services,
AI assists in tailoring treatment regimens according to each which lower outpatient and transportation expenditures for

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