AI-Assisted Antidiabetic Drug Consultation System for Glycemic Control in Type 2 Diabetes Patients Managed by Non-Specialist Physicians (AI-ADCS)
Clinical Validation of AI-assisted Antidiabetic Drug Consultation System-1
This study tests whether an artificial intelligence (AI) tool can help doctors choose better diabetes medicines for their patients. Type 2 diabetes is very common, but there are far more patients than diabetes specialists, so many patients are treated by doctors who are not diabetes specialists. The researchers built an AI consultation system that gives doctors real-time suggestions and predictions about diabetes medicines while they are prescribing. The doctor always makes the final decision.
In this trial, patients with type 2 diabetes whose blood sugar is not well controlled will be placed by chance (randomly) into one of two groups. In one group, the doctor uses the AI system when deciding on diabetes medicines. In the other group, the doctor prescribes as usual, without the AI system. All medicines used are already approved in Taiwan and given at approved doses.
The study follows each patient for 12 months, with check-ups at the start and at 3, 6, 9, and 12 months. The main goal is to compare how much the patients' long-term blood sugar level (HbA1c) improves between the two groups after one year. The researchers also look at how many patients reach their blood sugar target, how often low blood sugar happens, and whether any side effects occur. The aim is to find out whether using the AI tool leads to better blood sugar control.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Yi-Cheng Chang, M.D.
- Phone Number: 886+0972651027
- Email: b83401040@gmail.com
Study Contact Backup
- Name: Pan Hou Che, PhD student
- Phone Number: 886+0987197997
- Email: d12456001@g.ntu.edu.tw
Study Locations
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-
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Taipei, Taiwan, 100
- National Taiwan University Hospital
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Contact:
- Yi-Cheng Chang, M.D.
- Phone Number: 886+0972651027
- Email: b83401040@gmail.com
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Adults aged 18 to 80 years
- Diagnosis of type 2 diabetes for at least 6 months
- HbA1c above 8% within the past 3 months
- Currently using one or more oral antidiabetic drugs
- Able to understand and provide written informed consent
Exclusion Criteria:
- Pregnancy or breastfeeding
- Recent participation in another interventional clinical trial
- Cognitive impairment precluding understanding of the study
- Active cancer treatment within the past 6 years
- Use of systemic steroids
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Single
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental: AI-Assisted Prescribing
Non-specialist physicians prescribe antidiabetic medications after consulting the AI-assisted antidiabetic drug consultation system.
The system provides real-time, interactive prescribing recommendations, a drug-prioritization order, and outcome predictions.
The physician retains full control over the final prescribing decision.
All medications are approved in Taiwan and prescribed within approved dose ranges.
Patients are followed for 12 months.
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A machine-learning based clinical decision support software that provides non-specialist physicians with real-time, interactive antidiabetic prescribing recommendations, a drug-prioritization order, and outcome predictions (e.g., the predicted likelihood of reaching glycemic targets and responder/non-responder status for individual drugs).
The system was developed and validated using the NTUH integrated medical database platform.
It provides advisory recommendations only; the treating physician retains full control over the final prescribing decision.
All recommended medications are approved in Taiwan and within approved dose ranges.
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Active Comparator: Manual Prescribing (Non-AI)
Non-specialist physicians prescribe antidiabetic medications manually according to usual clinical practice, without using the AI consultation system.
All medications are approved in Taiwan and prescribed within approved dose ranges.
Patients are followed for 12 months.
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Antidiabetic medications prescribed manually by non-specialist physicians according to usual clinical practice, without using the AI consultation system.
All medications are approved in Taiwan and prescribed within approved dose ranges.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Change in HbA1c from baseline to 12 months
Time Frame: Baseline and 12 months
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The between-group difference in the change in glycated hemoglobin (HbA1c) from baseline to 12 months, comparing the AI-assisted prescribing arm with the manual prescribing (control) arm.
HbA1c reflects long-term glycemic control.
The primary analysis uses analysis of covariance (ANCOVA) adjusting for baseline HbA1c, following the intention-to-treat principle.
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Baseline and 12 months
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Proportion of participants achieving HbA1c < 7.0% at 12 months
Time Frame: 12 months
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The proportion of participants reaching the glycemic target of HbA1c below 7.0% at 12 months, compared between arms using chi-square tests.
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12 months
|
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Incidence of hypoglycemia over 12 months
Time Frame: Up to 12 months
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Incidence of hypoglycemic events over the 12-month follow-up, graded by severity: Level 1, glucose < 70 mg/dL (3.9 mmol/L) and ≥ 54 mg/dL (3.0 mmol/L); Level 2, glucose < 54 mg/dL (3.0 mmol/L); Level 3, severe hypoglycemia requiring assistance of another person regardless of glucose value.
Compared between arms using Poisson regression.
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Up to 12 months
|
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Incidence of prespecified adverse events over 12 months
Time Frame: Up to 12 months
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Incidence of prespecified adverse events over the 12-month follow-up, including urinary tract infection, lower-limb edema, signs of heart failure, fractures, nausea, vomiting, and diarrhea.
Compared between arms using Poisson regression.
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Up to 12 months
|
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Change in HbA1c from baseline at 3, 6, 9, and 12 months
Time Frame: Baseline, 3, 6, 9, and 12 months
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Baseline, 3, 6, 9, and 12 months
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Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 202507154RINA
- 115-X007 (Other Grant/Funding Number: National Taiwan University Hospital Yunlin Branch)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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