AI-Assisted Antidiabetic Drug Consultation System for Glycemic Control in Type 2 Diabetes Patients Managed by Non-Specialist Physicians

NCT07684690 | Not Yet Recruiting

• 2 other identifiers: 202507154RINA115-X007
• Study Type: interventional
• Target: 400
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Type 2 Diabetes Mellitus (T2DM)

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (4)

• Proportion of participants achieving HbA1c < 7.0% at 12 months

  12 months

• Incidence of hypoglycemia over 12 months

  Up to 12 months

• Incidence of prespecified adverse events over 12 months

  Up to 12 months

• Change in HbA1c from baseline at 3, 6, 9, and 12 months

  Baseline, 3, 6, 9, and 12 months

Study Arms (2)

AI-Assisted Prescribing

EXPERIMENTAL

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

Manual Prescribing (Non-AI)

ACTIVE COMPARATOR

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)

Interventions

AI-assisted antidiabetic drug consultation system DEVICE

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.

AI-Assisted Prescribing

Manual antidiabetic prescribing (without AI) OTHER

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.

Manual Prescribing (Non-AI)

Eligibility Criteria

• Age: 19 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• National Taiwan University Hospital: lead

Study Sites (1)

National Taiwan University Hospital

Taipei, 100, Taiwan

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 2

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Central Study Contacts

Yi-Cheng Chang, M.D.

CONTACT

886+0972651027; b83401040@gmail.com

Pan Hou Che, PhD student

CONTACT

886+0987197997; d12456001@g.ntu.edu.tw

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Prospective, single-center, parallel-group randomized controlled trial. Eligible patients are randomly allocated 1:1 to AI-assisted prescribing versus conventional prescribing, using stratified randomization balancing age, sex, and baseline HbA1c. Follow-up is 12 months.

Study Record Dates

• First Submitted: June 29, 2026
• First Posted: July 6, 2026
• Study Start: July 1, 2026
• Primary Completion (Estimated): November 1, 2027
• Study Completion (Estimated): November 1, 2027
• Last Updated: July 6, 2026

Record last verified: 2026-06

Locations

TW (1)