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

Official title

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

Overview

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.

Interventions

• 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
• 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.

Primary outcome measures

• Change in HbA1c from baseline to 12 months [Time frame: Baseline and 12 months]

Secondary outcome measures (4)

• Proportion of participants achieving HbA1c < 7.0% at 12 months [Time frame: 12 months]
• Incidence of hypoglycemia over 12 months [Time frame: Up to 12 months]
• Incidence of prespecified adverse events over 12 months [Time frame: Up to 12 months]
• Change in HbA1c from baseline at 3, 6, 9, and 12 months [Time frame: Baseline, 3, 6, 9, and 12 months]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Single blind

Primary purpose

Treatment

Study locations

Taiwan · 1 center

• National Taiwan University Hospital — Taipei

Identifiers

NCT: NCT07684690 · 202507154RINA · 115-X007

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗