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NCT Number: NCT07684690

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

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.

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Key information

Conditions

Type 2 Diabetes Mellitus (T2DM) Diabetes Mellitus Diabetes Mellitus, Type 2 Endocrine System Diseases Glucose Metabolism Disorders Metabolic Diseases Nutritional and Metabolic Diseases

Age range

19 year–80 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

National Taiwan University Hospital

Taipei, 100, Taiwan

Location contact

Yi-Cheng Chang, M.D.

CONTACT

[email protected]

886+0972651027

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

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.

Primary outcomes

  1. Change in HbA1c from baseline to 12 months

    Time frame: Baseline and 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.

Secondary outcomes

  1. Proportion of participants achieving HbA1c < 7.0% at 12 months

    Time frame: 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.

  2. Incidence of hypoglycemia over 12 months

    Time frame: Up to 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.

  3. Incidence of prespecified adverse events over 12 months

    Time frame: Up to 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.

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

    Time frame: Baseline, 3, 6, 9, and 12 months

Study contacts

Contact information is provided by the study sponsor or research team.

Pan Hou Che, PhD student

CONTACT

[email protected]

886+0987197997

Yi-Cheng Chang, M.D.

CONTACT

[email protected]

886+0972651027

Sponsors and collaborators

Lead sponsor

National Taiwan University Hospital

Other

Registry information

Official study title

Clinical Validation of AI-assisted Antidiabetic Drug Consultation System-1

Acronym: AI-ADCS

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Jul 6, 2026
Registry last updated
Jul 6, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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