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THE IMPACT OF ARTIFICIAL INTELLIGENCE ON CLINICAL PRACTICE IN DIABETES MANAGEMENT Muzammil Mohammed Shadan Institute of Medical Sciences, Hyderabad, Telangana, India Background: Artificial Intelligence (AI) is revolutionizing diabetes management by enhancing early diagnosis, optimizing treatment plans, and enabling real-time monitoring. AI-driven tools improve clinical decision-making, reduce complications, and enhance patient adherence, ultimately transforming diabetes care. Aims and Objective: This research aims to investigate the impact of Artificial Intelligence (AI) on diabetes clinical practice, particularly its role in facilitating early diagnosis, tailoring treatment, enabling real-time monitoring, and enhancing decision-making processes. The study intends to evaluate the statistical significance of AI-based interventions and their effectiveness in improving patient outcomes, alongside a comprehensive examination of modifications in clinical protocols and the quality of patient care. Methodology: A retrospective analysis was conducted using clinical data from multiple healthcare facilities that have adopted AI technologies in diabetes management. The review encompassed AI-enhanced glucose monitoring systems, predictive models for disease progression, and individualized treatment strategies. Data were gathered from clinical trials, patient records, and AI-supported decision-making tools over a five-year timeframe. Statistical evaluations were performed using multivariate regression to assess the impact of AI on lowering HbA1c levels, enhancing treatment adherence, and mitigating diabetesrelated complications. Results were analysed by comparing AI-assisted care with traditional treatment approaches. Results: The analysis revealed a statistically significant enhancement in patient outcomes associated with the integration of AI in diabetes management. Patients utilizing AI-based monitoring systems experienced an average HbA1c level reduction of 1.2%, with a p-value of less than 0.01, indicating statistical significance. AI-driven predictive models demonstrated an 85% accuracy rate in anticipating diabetes progression, facilitating timely interventions, and decreasing the incidence of severe complications, such as diabetic retinopathy, by 40%. Furthermore, AI personalized treatment algorithms resulted in a 25% increase in medication adherence, supported by an odds ratio of 2.5 (95% CI: 1.9–3.1), suggesting that patients engaged in AI-supported interventions were significantly more likely to adhere to prescribed treatment plans compared to those receiving traditional care. AI tools have significantly improved clinical decision-making processes. The integration of real-time data from AI-assisted glucose monitoring systems has enhanced clinician responsiveness to both hypo- and hyperglycaemic incidents, leading to a 30% reduction in emergency hospitalizations. Additionally, the implementation of AI-enabled decision support systems has allowed clinicians to identify optimal treatment plans more swiftly, decreasing the average time required to achieve glycaemic control by 20%. This not only accelerates patient recovery but also alleviates the strain on healthcare resources. The statistical analysis of these outcomes demonstrates a high level of accuracy in predicting and enhancing clinical results. Conclusion: This study’s findings indicate that AI has profoundly transformed diabetes clinical practice, resulting in significant improvements in patient outcomes, adherence to treatment, and overall healthcare efficiency. AI-driven tools provide a tailored approach to diabetes management, enabling early diagnosis, predictive modelling of disease progression, and continuous monitoring. These advancements not only improve the accuracy of clinical decision-making but also help mitigate long-term complications related to diabetes. As AI technology continues to advance, its influence on chronic disease management is expected to grow, offering even greater advantages for both patients and healthcare providers. Keywords: Artificial intelligence in diabetes, clinical decision support, predictive analytics INTEGRATING REST STRATEGIES AND AI-DRIVEN TOOLS TO ENHANCE DIABETOLOGIST PRODUCTIVITY AND WELLNESS Harsh Atul Hirani, Alok Modi1, Dr. Bharat Saboo2 Life Care Centre for Diabetes, Hyderabad, Telangana, 1Dr. Alok Modi’s Diabetes Center and Kevalya Hospital, Thane, Maharashtra, 2Prayas Diabetes Center, Indore, Madhya Pradesh, India Background: Diabetologists face significant challenges, including managing extensive clinical workloads, adapting to rapid advancements in diabetes care, and addressing the emotional toll of chronic disease management. Burnout among diabetologists negatively impacts their personal well-being and the quality of care delivered to patients. There is a pressing need to integrate wellness strategies with innovative technological solutions to support diabetologists in sustaining high performance and improving patient outcomes. Aim and Objective: To assess the effectiveness of combining diversified rest strategies with AI-driven tools in enhancing the productivity, wellness, and patient care outcomes of diabetologists. Methodology: This study reviewed wellness practices and their applicability to diabetologists, focusing on seven types of rest: physical, mental, emotional, spiritual, social, sensory, and creative. Tailored interventions were developed for each rest type, incorporating AI-driven tools such as Large Language Models (LLMs), wearable devices, and mindfulness applications. Key outcomes were analyzed in terms of their impact on burnout reduction, productivity improvement, and patient satisfaction. Methods and specific strategies for each type of rest are detailed in Figure 1.Figure 1: Methods of rest, which outlines various strategies for achieving different types of rest: physical, mental, emotional, spiritual, social, sensory, and creative, along with supportive technologies and practices for eachResults: The integration of rest strategies and AI tools produced notable benefits. Wearable devices optimized physical activity and sleep patterns, improving focus and reducing fatigue. LLMs alleviated cognitive overload, supporting decision-making and administrative efficiency. Emotional resilience improved through AI-based journaling and coaching, while mindfulness apps enhanced mental clarity and reduced stress. Networking platforms facilitated meaningful social connections, and brainstorming tools fostered innovation, aiding creative rest. These interventions collectively enhanced work-life balance, reduced burnout, and improved patient care outcomes. Conclusion: Integrating rest strategies with AI-driven tools offers a transformative approach to addressing burnout and enhancing productivity among diabetologists. By fostering sustainable well-being practices, this framework ensures improved physician wellness and better diabetes care delivery. Keywords: Artificial intelligence, burnout prevention, diabetologist wellness, rest strategies, technological solutions, work-life balance NOVEL MARKERS FOR EARLY ONSET OF DIABETES: EVALUATION OF GLYCAEMIC VARIABILITY IN TYPE- 2 DIABETES MELLITUS USING CONTINUOUS GLUCOSE MONITORING SYSTEM: A PILOT STUDY Ravi Kumar, Santosh Kumar Singh Department of Internal Medicine, Armed Forces Medical College, Pune, Maharashtra, India Background: Continuous glucose monitoring (CGM) systems allow us to perform real-time monitoring of blood glucose levels. It can be used for assessment of the Glycemic Variability (GV) in subjects who are at high risk for the development of Diabetes, which can be taken as an early marker of the onset of diabetes. Aimand Objective: To detect glycaemic variability as an early marker for derangement in blood sugar using the CGM system in medium-high-risk subjects for diabetes. To evaluate GV in individuals with medium-high risk groups for type-2 diabetes mellitus and to find the association of GV with beta-cell dysfunction and Insulin resistance. Methodology: The study was an analytic cross-sectional study, done in a tertiary care hospital in western Maharashtra. Since this was a pilot study a sample size of 60 was taken. All patients reporting to OPD who are in medium or high-risk groups for Diabetes as per the IDRS were interviewed, their anthropometric parameters, and basic laboratory parameters were taken and the CGM system (Abbot freestyle Libre) (> 3 days -14 days) were attached to access the parameters of GV. The GV parameters were calculated using the standard software, and HOMA-IR & HOMA-B were calculated using the standard formulas. The data tabulated in the Excel sheets were evaluated using the statistical methods and software (SPSS) to access the prevalence of GV using its parameters i.e. early dysglycemia in mediumhigh-risk individuals for diabetes. Results: The various parameters GV like mean, standard deviation, LI, J index, HBGI, CONGA, and MAGE, were found to be high in the study population and with a statistically significant correlation between the various parameters of glycemic variability [Scatter plot diagram of the various parameters of GV is as attached as Figure 1].Figure 1: Scatter plot diagram of the various parameters of GVConclusions: The present study has helped us in the early detection of diabetes in patients having moderate to high risk for diabetes and will adequate preventive and therapeutic interventions will be advised to the individuals for management and prevention of complications. Need for more similar studies in various patient subsets of healthy individuals, and patients with chronic diabetes. Keywords: CGM, glycaemic variability, type-2 diabetes mellitus RETROSPECTIVE STUDY CORRELATING SELF MONITORING BLOOD GLUCOSE (SMBG) VALUES WITH HBA1C Abhisekh Raha Lumding Divisional Railway Hospital, Indian Railway Health Services, Lumding, Assam, India Background: Self‐monitoring of blood glucose (SMBG) plays an important role in the management of Type I Diabetes. SMBG has many proven benefits in Type 1 diabetics such as, minimizing glucose variability, helping to predict severe hypoglycemia and aiding the achievement of hemoglobin A1c (HbA1c) targets. Aim and Objective: Correlating HbA1c with multiple SMBG values in persons with Type 1 DM. Methodology: Retrospective analysis of 10 persons’ SMBG values with Type I DM above the age of 18,who were measuring their pre‐meal and bedtime sugars regularly (more than 20 times per week) was done. SMBG values between 70 to 180mg/dl were considered Points In Range (PIR). The mean HbA1c values of these persons were observed and correlated with SMBG data. Results: The SMBG values were more than 70% in range in 2 out of 10 persons. The mean HbA1c of these persons was 6.9%. 4 persons with 65% to 70% PIR had mean HbA1c of 7.2%, 2 persons with 60 to 65% PIR had mean HbA1c of 7.5%, 1 person with 55 to 60% PIR had mean HbA1c of 8.1% and 1 persons with 45 to 50% PIR had mean HbA1c of 8.4%. Conclusions: PIR correlates well with HbA1c in persons with Type I DM who measure their blood glucose more than 3 times in a day. Keywords: HbA1C, point in range, SMBG USAGE OF TECHNOLOGY IN GLYCEMIC CONTROL FOR RURAL PEOPLE WITH DIABETES Abhisekh Raha Lumding Divisional Railway Hospital, Indian Railway Health Services, Lumding, Assam, India Background: In a technological triad model, a connection is established between doctor and two voluntary workers to monitor remotely the patient’s glycemic levels and educate the patients online via conference video calls following up and daily monitoring the regular treatment and diet intake. Aim and Objective: Here, we studied the role of teamwork in achieving glycemic control in rural patients using a cost-effective technological triad model. Methodology: 65 people with diabetes were selected based on inclusion criteria, out of which only 30 agreed to participate in the study. Inclusion criteria: Men and women aged between 30 to 60 years with type 2 diabetes mellitus (T2DM), having an uncontrolled fasting blood sugar (FBS), post prandial blood sugar (PPBS) and HbA1c between 7.1 - 10.5 %. Exclusion criteria: Type 1 diabetes mellitus (T1DM), pre-existing renal, hepatic, or cardiac disease, HbA1c > 10.5 %. Tie up was made with the two voluntary worker to monitor remotely the patient’s glycemic levels and educate the patients online via conference video call following up and daily monitoring the regular treatment and diet intake. A technological triad was established two voluntary workers and the treating doctor. Results: 30 patients were randomly divided into two groups, i.e., group A and B which comprised of 15 patients each. Parameters such as FBS, PPBS, HbA1c and lipid profile, complete blood count, complete urinary analysis, Liver function test, Serum Creatinine, BMI were collected on day 1 and follow up data which includes measurement of FBS, PPBS, HbA1c and lipid profile were collected at 3 months and at 6 months. Data obtained was measured with SPSS version 17 software. A 10 were on daily remotely with the help of worker for diet regular of a in 6 months the B were on day 1 and these patients of B were the of diet and these groups were on in the for There was statistically significant in the mean FBS, PPBS, HbA1c and lipid profile at the of the study. the follow up 3 months was observed that the mean FBS, and were significantly in the group A 6 months was observed that mean FBS, PPBS, and was significantly in group A There was statistically significant in the mean levels between Conclusion: technological has a of to is a that the of and the not only for the patient but also for the care A with a physician not achieve glycemic control as in group B compared to a approach and monitoring that the adherence to medication and diet as demonstrated in group Keywords: Health rural technology triad FOR AND USING AI Hospital, India Background: and age related in patients with diabetes, for multiple can in early The intelligence (AI) on the on can for and The of AI for in diabetes care Aim and Objective: To for and using the AI in patients with diabetes. Methodology: This was a study of patients with diabetes at a tertiary care hospital in India using the were analyzed by the for each Results: In this study were was present in in and or in Patients for at 1 or 2 was and disease was in Conclusion: in patients had and 1 in 2 for at 1 in diabetes care can significantly improve early detection and management of multiple This study the need for to while treating patients with diabetes. The AI offers an to this approach and Keywords: comprehensive diabetic retinopathy, AI OF CONTINUOUS GLUCOSE MONITORING (CGM) IN GLYCEMIC CONTROL OF DIABETES IN A STUDY IN THE OF Department of Medicine, Hospital, Hospital, India Background: Continuous glucose monitoring (CGM) systems have as a for managing diabetes, real-time glucose data and that better glycemic This study the effectiveness of CGM systems in improving glycemic control among patients with diabetes in care Aim and Objective: the effectiveness of CGM in improving glycemic control among diabetes patients in a care the impact of CGM on diabetes management and patient adherence to treatment plans in the of the role of CGM in reducing complications associated with diabetes, such as glycemic control and quality of To evaluate the of care to CGM into clinical and its on their Methodology: A was conducted patients with type 1 and type 2 diabetes care were randomly to a CGM group or a control group using traditional of blood glucose HbA1c levels, of and outcomes on quality of were conducted at and months. Results: Patients in the CGM group a significant reduction in HbA1c levels compared to the SMBG group reduction of The CGM group also experienced per compared to the SMBG group per Additionally, outcomes and improved quality of among CGM Conclusion: The study demonstrates that CGM systems significantly improve glycemic control and reduce in care CGM also greater with their diabetes management. These findings that CGM into care practices can enhance diabetes management and improve patient outcomes, a for implementation in care Keywords: Continuous glucose monitoring, diabetes management, glycemic patient care OF IN DIABETES IN 2 Diabetes India Background: The of solutions to reduce and to and personal as well as has in years The treatment and care of patients with diabetes and its and regular monitoring and by solutions to standard in and provide access to diabetes care. Aim and Objective: The of this study was to the and glycemic in patients who up as their treatment in diabetes care. Methodology: A retrospective analysis was done of the patients in Diabetes at diabetes care A and follow up was done for months along with through from to Patients were in person 6 months as per Parameters like in of patients their follow up and adherence and were Results: It was observed that out of reduction of A1c was in of patients Conclusion: to patient care through the glycemic control and overall well of the patients. Keywords: type 2 diabetics INTEGRATING TOOLS IN DIABETES THE IN CLINICAL Health India Background: Type Diabetes is a chronic in which personalized disease management and adherence to The Diabetes for the of Diabetes the of patient’s approach for management. a personalized interventions with Aim and Objective: To evaluate the impact of the in managing clinical outcomes for individuals with Methodology: This study data from 20 patients in personalized diet and plans, glucose and regular cognitive The in in Range Range Range fasting blood glucose and blood glucose was evaluated as outcomes and in lipid and as outcomes. Results: The mean age of patients in the study was with a of the mean HbA1c significantly from to with and of and of patients were in the which to 85% by the of the study. Patients who improved their a mean HbA1c reduction of Additionally, from to while at compared to Additionally, a reduction in and reduction in BMI was Conclusion: The significant impact on clinical outcomes, along with high patient adherence, as a therapeutic that and the standard of care for management. Keywords: Diabetes care, AND CLINICAL IMPACT OF THE ON AND GLYCEMIC CONTROL IN Health India Background: Type 2 Diabetes a significant and healthcare a focus on both glycemic control and management. like Continuous (CGM) with platforms increase patient by real-time glucose The Glycemic in Diabetes this approach by personalized patient care. Aim and Objective: To evaluate the effectiveness of the in facilitating improvements in quality of and Health Methodology: This study 20 patients with who were by the physician to be the were personalized plans, and cognitive with CGM The outcomes measured were improvements in evaluated through reduction in the of an increase in and parameters such as outcomes evaluated through in and hospital Results: This study demonstrated with 85% of patients reducing and an average reduction of These were in better glycemic with HbA1c reduction observed in of is by in management and sleep quality by and Insulin were reduced in of and the between was for 25% of patients who an HbA1c of more than Conclusion: The in improving glycemic and in offers a to enhance outcomes and patient care. Keywords: Diabetes, glycemic A RETROSPECTIVE STUDY OF OF IN AND USING TOOLS Hospital, India Background: is of the complications of Diabetes and a leading of and as disease which leading to and in of DM glycemic with associated are the is on and as and and based on of and disease for early detection using as AI is revolutionizing management this study to incidence in and identify healthcare and Aim and Objective: for early detection using as AI is revolutionizing management this study to incidence in and identify healthcare and Methodology: A Retrospective study was conducted with data of patients from and from All were this study through examination with the help of AI software. such as of diabetes were taken into and and were calculated between and Results: and were both out of and were both out of Patients with also had significantly disease and HbA1c values and a prevalence of such as Conclusion: This a of in population compared to to glycemic of diabetes and of The findings the need in managing complications AI tools and predictive analytics for early detection risk and By AI a role in healthcare Keywords: Artificial intelligence, diabetic retinopathy, AI AND - A TO DIABETES MANAGEMENT of for India Background: Diabetes and have levels with people with diabetes in and indicating this will to by of with many individuals of their The is with healthcare in and to by healthcare systems focus on care, a need for innovative preventive strategies. and AI-driven transformative tools for personalized interventions, enabling real-time monitoring and predictive for diabetes prevention and management. This study a that these technologies while incorporating a for are not Aim and Objective: this study is to focus on & the in this innovative Methodology: A of with diabetes or evaluated the of a Health & This to AI-driven for personalized and technology for real-time and levels were Results: of that adherence to through AI 85% in AI-driven to blood sugar levels and improve 70% found the of for the impact of on be to to their were not demonstrated a of healthy by and personalized Conclusion: this of we are to a of this approach on this and This approach has the to care for by combining with studies its through and its be on and for more and This approach has the to care for by combining with studies its through and its be on and for more Keywords: AI coaching, intelligence, CGM, diabetes, analysis, predictive care AI and Madhya Pradesh, India Background: The the effectiveness of personalized interventions in improving intelligence (AI) platforms like were used in for but their in their in care. To these we developed algorithms and for AI to which are to improve Aim and Objective: The of this study is to investigate the effectiveness of of AI in improving compared to by a Methodology: with type-2 diabetes from diabetes years and glycaemic from to were in the study. group comprised subjects age years and control group 15 subjects age was Serum and were compared at the and of study A the of control group for group was by a a software to and these to Results: and control group statistically significant in study parameters but group performed better as compared to control 1: parameters with intelligence parameters with by Intelligence with in improving in care Keywords: Artificial intelligence, diabetes, personalized AI-DRIVEN EVALUATION OF FOR DIABETES of for India Background: a role in managing diabetes by improving glycemic and the variability in and of their Artificial Intelligence evaluations an innovative approach to and This study AI algorithms to evaluate the of different in diabetes management, offering into glycemic outcomes and Aim and Objective: The of this study is to the effectiveness of various including the and in improving glycemic control and through AI-driven Methodology: AI-driven and of trials, and studies between and was The analysis measured the impact of on fasting glucose and outcomes. and were calculated using Results: HbA1c reduction of (95% CI: to and improved reduction of (95% CI: to with significant and HbA1c reduction of (95% CI: to with benefits. HbA1c reduction of (95% CI: to with reduction of and improvements in glycemic control and with better adherence Conclusion: AI-driven that the and provide the glycemic The analysis the of personalized interventions in diabetes management. research platforms and data to and This study the transformative role of AI in studies are required to AI-driven and their applicability technology with continuous glucose monitoring systems enhance personalized strategies for optimal outcomes. Keywords: AI diabetes management, patterns, glycemic personalized