Type 2 Diabetes Mellitus (T2DM)AI-ADCSNCT07684690
AI-Assisted Antidiabetic Drug Consultation System for Glycemic Control in Type 2 Diabetes Patients Managed by Non-Specialist Physicians
A study testing AI-assisted antidiabetic drug consultation system and Manual antidiabetic prescribing (without AI) in people with Type 2 Diabetes Mellitus (T2DM), run by National Taiwan University Hospital at 1 site in Taiwan.
Trial 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. Read more about this study
- Who is it for?
- Ages 19 to 80 years
- Conditions the study is for
- Type 2 Diabetes Mellitus (T2DM)
- What is being tested?
- AI-assisted antidiabetic drug consultation system and Manual antidiabetic prescribing (without AI)
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 400 participants
- Where
- 1 siteTaiwan
- Study dates
- Jul 2026to Nov 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 19 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site
National Taiwan University Hospital
Taipei, 100
Yi-Cheng Chang, M.D., 886+0972651027, b83401040@gmail.com
About this study
Official title: 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.
- Conditions
- Type 2 Diabetes Mellitus (T2DM)
- Being tested
- AI-assisted antidiabetic drug consultation system, Manual antidiabetic prescribing (without AI)
- Medical subject headings
- Diabetes Mellitus, Type 2
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted antidiabetic drug consultation systemDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Other: Manual antidiabetic prescribing (without AI) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline and 12 months
Secondary outcomes (4)
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.
Measured: 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.
Measured: 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.
Measured: Up to 12 months
Change in HbA1c from baseline at 3, 6, 9, and 12 months
Measured: Baseline, 3, 6, 9, and 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jul 2026
- Study startJul 2026 (estimated)
- Primary completionNov 2027 (estimated)
- Study completionNov 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Type 2 Diabetes Mellitus (T2DM)Studies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- 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.
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07684690
- Sponsor's protocol ID
- 202507154RINA
- Other IDs
- 115-X007
- Acronym
- AI-ADCS
- Status last verified
- Jun 2026
- First submitted
- 29 Jun 2026
- Last updated
- 6 Jul 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 6 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Validation of AI-assisted Antidiabetic Drug Consultation System-1
- ClinicalTrials.gov ID
- NCT07684690
- Acronym / sponsor ID
- AI-ADCS / 202507154RINA
- Conditions
- Type 2 Diabetes Mellitus (T2DM)
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Study start
- Jul 2026 (estimated)
- Primary completion
- Nov 2027 (estimated)
- Study completion
- Nov 2027 (estimated)
- First posted
- 6 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Prescribing | Experimental | Device: AI-assisted antidiabetic drug consultation systemNon-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. |
| Manual Prescribing (Non-AI) | Active comparator | Other: Manual antidiabetic prescribing (without 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. |
Who may qualify
- Ages
- 19 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-assisted antidiabetic drug consultation systemA 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. | Device | AI-Assisted Prescribing |
| 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. | Other | Manual Prescribing (Non-AI) |
Primary outcome
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.
4 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jul 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-assisted antidiabetic drug consultation system and Manual antidiabetic prescribing (without AI) in people with Type 2 Diabetes Mellitus (T2DM), run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 6 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yi-Cheng Chang, M.D. (886+0972651027, b83401040@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in HbA1c from baseline to 12 months (time frame: Baseline and 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Jul 2026.