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: Not yet recruiting
• Conditions: Type 2 Diabetes Mellitus (T2DM)
• Intervention / Treatment: Device: AI-assisted antidiabetic drug consultation system; Other: Manual antidiabetic prescribing (without AI)

• Study Type: Interventional
• Enrollment (Estimated): 400
• Phase: Not Applicable

Contacts and Locations

• 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

• Taiwan
  • Taipei, Taiwan, 100
    • National Taiwan University Hospital
    • Contact: Yi-Cheng Chang, M.D.; Phone Number: 886+0972651027; Email: b83401040@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

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

2

Arms and Interventions

Participant Group / Arm	Intervention / 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.	Device: AI-assisted antidiabetic drug consultation system; 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.
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.	Other: Manual antidiabetic prescribing (without AI); 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in HbA1c from baseline to 12 months	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.	Baseline and 12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Proportion of participants achieving HbA1c < 7.0% at 12 months	The proportion of participants reaching the glycemic target of HbA1c below 7.0% at 12 months, compared between arms using chi-square tests.	12 months
Incidence of hypoglycemia over 12 months	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.	Up to 12 months
Incidence of prespecified adverse events over 12 months	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.	Up to 12 months
Change in HbA1c from baseline at 3, 6, 9, and 12 months		Baseline, 3, 6, 9, and 12 months

Collaborators and Investigators

• Sponsor: National Taiwan University Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): July 1, 2026
• Primary Completion (Estimated): November 1, 2027
• Study Completion (Estimated): November 1, 2027

Study Registration Dates

• First Submitted: June 29, 2026
• First Submitted That Met QC Criteria: June 29, 2026
• First Posted (Actual): July 6, 2026

Study Record Updates

• Last Update Posted (Actual): July 6, 2026
• Last Update Submitted That Met QC Criteria: June 29, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Endocrine System Diseases; Metabolic Diseases; Glucose Metabolism Disorders; Diabetes Mellitus; Nutritional and Metabolic Diseases; Diabetes Mellitus, Type 2
• 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)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No