Generative AI-Based Simulation for Diagnostic Communication in Type 2 Diabetes (DIALOGUE-DM2)
NCT ID: NCT07252193
Last Updated: 2025-12-29
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
120 participants
INTERVENTIONAL
2025-09-22
2025-12-20
Brief Summary
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A total of 120 medical students are randomized to either an intervention group using the DIALOGUE-DM2 AI simulation platform or a control group following traditional educational methods. Participants complete a pre-test, receive training according to group assignment, and then undergo a post-test evaluation.
The primary outcome is improvement in diagnostic communication skills, measured by standardized patient scenarios and validated rubrics. Secondary outcomes include self-reported confidence, communication domains, and inter-rater agreement between faculty evaluators and AI scoring.
This trial aims to provide high-quality evidence on the potential of generative AI to enhance communication training in medical education, specifically in the context of type 2 diabetes diagnosis.
Detailed Description
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Design:
The study is a randomized, blinded, parallel-group, controlled trial conducted at the Faculty of Higher Studies Iztacala (FES Iztacala), UNAM. A total of 120 medical students are enrolled and randomized (1:1) into either the intervention group (AI-based simulation training) or the control group (traditional training with standardized patients and faculty feedback).
Intervention:
* Intervention group: Students interact with the DIALOGUE-DM2 platform, which provides generative AI-driven simulated patients. They complete multiple diagnostic disclosure scenarios and receive immediate feedback on performance, based on standardized communication rubrics.
* Control group: Students receive standard training, including lectures and supervised practice with peer role-play and faculty-guided feedback.
Assessments:
* Pre-test: All students complete one standardized patient scenario with faculty and AI evaluation prior to intervention.
* Training phase: Participants complete their assigned training (AI vs. standard).
* Post-test: Students complete a standardized diagnostic disclosure scenario. Independent faculty evaluators (blinded to group assignment) and the AI platform score performance.
Outcomes:
* Primary outcome: Change in diagnostic communication performance score from pre-test to post-test, measured by validated rubrics (Kalamazoo framework, MRS).
* Secondary outcomes:
* Student self-assessment of communication confidence.
* Domain-specific improvements (information delivery, empathy, risk explanation, shared decision-making).
* Agreement between human evaluators and AI scoring.
Ethics and Oversight:
The study has been reviewed and approved by the Research Ethics Committee of FES Iztacala, UNAM (Approval Number CE/FESI/042025/1915). Risks are minimal, as the intervention is educational and non-invasive.
Significance:
This is the first randomized controlled trial in Mexico to evaluate a generative AI-based simulation for diagnostic communication. Results will inform the integration of AI-driven training tools into medical education curricula and could contribute to scalable innovations in the training of healthcare professionals for chronic disease management, starting with type 2 diabetes.
Conditions
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Keywords
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Study Design
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RANDOMIZED
PARALLEL
HEALTH_SERVICES_RESEARCH
TRIPLE
Study Groups
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AI-Based Simulation Training (DIALOGUE-DM2)
Medical students assigned to this arm will receive training using the DIALOGUE-DM2 platform, which provides generative AI-driven simulated patients. Participants will engage in multiple diagnostic disclosure scenarios focused on type 2 diabetes and receive immediate feedback generated by the AI system. Feedback is aligned with validated communication frameworks (Kalamazoo, MRS). Training is conducted over several sessions prior to the post-test evaluation.
AI-Based Simulation Training (DIALOGUE-DM2)
Medical students interact with the DIALOGUE-DM2 platform, a generative AI-based simulation system. The platform delivers virtual patient encounters focused on type 2 diabetes diagnostic disclosure. Students complete multiple simulated scenarios and receive immediate AI-generated feedback aligned with standardized communication rubrics (Kalamazoo, MRS). Training aims to enhance diagnostic communication skills prior to post-test evaluation.
Traditional Training
Medical students assigned to this arm will receive traditional communication skills training. This includes lectures, peer role-play, and faculty-supervised feedback sessions covering diagnostic disclosure in type 2 diabetes. Participants will complete the same number of training sessions as the intervention group before the post-test evaluation.
Traditional Training
Medical students receive traditional training in diagnostic communication. This includes lectures, peer role-play, and faculty-supervised feedback sessions covering diagnostic disclosure in type 2 diabetes. The training duration and number of sessions are matched to the intervention group.
Interventions
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AI-Based Simulation Training (DIALOGUE-DM2)
Medical students interact with the DIALOGUE-DM2 platform, a generative AI-based simulation system. The platform delivers virtual patient encounters focused on type 2 diabetes diagnostic disclosure. Students complete multiple simulated scenarios and receive immediate AI-generated feedback aligned with standardized communication rubrics (Kalamazoo, MRS). Training aims to enhance diagnostic communication skills prior to post-test evaluation.
Traditional Training
Medical students receive traditional training in diagnostic communication. This includes lectures, peer role-play, and faculty-supervised feedback sessions covering diagnostic disclosure in type 2 diabetes. The training duration and number of sessions are matched to the intervention group.
Eligibility Criteria
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Inclusion Criteria
* Age between 18 and 30 years.
* Able to provide informed consent.
* Willing to participate in all study phases (pre-test, intervention, post-test).
Exclusion Criteria
* Previous formal training in diagnostic communication beyond the standard medical curriculum.
* Incomplete availability for scheduled sessions.
* Refusal or inability to provide informed consent.
18 Years
29 Years
ALL
Yes
Sponsors
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Universidad Nacional Autonoma de Mexico
OTHER
Responsible Party
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Héctor Iván Saldívar Cerón
Principal Investigator, FES Iztacala, UNAM
Locations
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Universidad Nacional Autónoma de México, Faculty of Higher Studies Iztacala (FES Iztacala)
Tlalnepantla, , Mexico
Countries
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References
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Suarez-Garcia RX, Chavez-Castaneda Q, Orrico-Perez R, Valencia-Marin S, Castaneda-Ramirez AE, Quinones-Lara E, Ramos-Cortes CA, Gaytan-Gomez AM, Cortes-Rodriguez J, Jarquin-Ramirez J, Aguilar-Marchand NG, Valdes-Hernandez G, Campos-Martinez TE, Vilches-Flores A, Leon-Cabrera S, Mendez-Cruz AR, Jay-Jimenez BO, Saldivar-Ceron HI. DIALOGUE: A Generative AI-Based Pre-Post Simulation Study to Enhance Diagnostic Communication in Medical Students Through Virtual Type 2 Diabetes Scenarios. Eur J Investig Health Psychol Educ. 2025 Aug 7;15(8):152. doi: 10.3390/ejihpe15080152.
Other Identifiers
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UNAM-DIALOGUE-DM2-2025
Identifier Type: -
Identifier Source: org_study_id