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Evaluation of the Therapeutic Efficacy and Safety of Artificial Intelligence-based Decision-making Technology in the Integrated Management of Diabetes Mellitus: a Longitudinal, Open-labeled, Randomized Controlled Trial
Purpose: To evaluate the efficacy of artificial intelligence (AI)-based decision-making technology in managing glycated hemoglobin (HbA1c) and blood glucose levels compared to the control group. Methods: For the AI Intervention group, the patients will be trained to independently use the diabetes telemedicine platform application. Each patient will be equipped with a glucometer and exercise bracelet, and the data will be automatically transmitted to the medical server via Bluetooth. The healthcare platform will analyze the uploaded data and provide feedback suggestions on medication, diet, and exercise automatically. The platform will also monitor the medical and lifestyle data of the patients every two weeks, offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform. The platform is a digitally integrated healthcare platform that patients can use independently without the need for monitoring and assistance by healthcare professionals. The glucometer and pedometer bracelet will automatically connect to the platform through Bluetooth. The patient lab sheet identification and structured conversion system, AI for food picture identification and calorie calculation systems, and the AI decision-making system are on the cloud server. Patients upload image information, such as lab sheets and meal pictures, through the patient's diabetes mobile health system, and the cloud platform intelligently analyzes the patient's disease, medication, and daily life status to develop personalized solutions according to individual control goals. Free outpatient visits will be provided to both the intervention and control groups every twelve weeks. For the conventional treatment group, patients will receive a free blood glucometer and will have regular outpatient appointments. There is no limit to the number of outpatient visits; however, they are required to regularly monitor and record their blood glucose, diet, and exercise data to ensure that the medical team objectively conduct their diagnosis and treatment activities. The medical team will provide free outpatient visits every 12 weeks, along with advice on medication, diet, and exercise based on the individual's blood glucose level. Expected results: A significant difference in HbA1c change from baseline to 48 weeks and improved FPG and 2-hour postprandial blood glucose levels in the AI intervention group were observed.
Follow-up Plan:: Visit 1(-4W\~-1W): Obtain the written informed consents of the patients, conduct the demographic survey, medical record survey, drug history investigation, subject compliance investigation, vital signs checkup, laboratory tests, imaging, and other instrument examinations, as well as evaluate the comorbidities of diabetes. Visit 2 (D0): Educate the intervention group operating the platform system, evaluating diabetic hypoglycemia events, enhancing patients' self-management abilities, and knowledge mastery. Lab tests will be conducted at 12-week intervals, including Visit 3 (12W), Visit 4 (24W) or Visit 5 (36W), and Visit 6 (48W).
Age
18 - 75 years
Sex
ALL
Healthy Volunteers
No
The First Hospital of Jilin University
Changchun, Jilin, China
Start Date
June 15, 2025
Primary Completion Date
December 1, 2026
Completion Date
December 1, 2026
Last Updated
December 9, 2025
400
ESTIMATED participants
artificial intelligence
OTHER
Routine diagnosis and treatment group for diabetes
OTHER
Lead Sponsor
The First Hospital of Jilin University
NCT07079592
NCT07236840
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