Evaluating Conversational Artificial Intelligence for Depression Management

Part of paid clinical trials in Fairfax, Virginia.

Sponsor
George Mason University
Study ID
NCT07105397
Status
Not Yet Recruiting

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Conditions

  • Major Depressive Disorder (MDD)

Eligibility Criteria

Sex
ALL
Age
18 Years - 85 Years
Healthy Volunteers
Not accepted

Interventions

  • Conversational AI system vs Usual Care — OTHER
    Participants complete medical history intake through an interactive conversational AI designed to support patient-centered, empathetic dialogue. Using large language models (LLM), the system interprets patient input, maintains context, and generates natural-language responses. A dialogue manager prioritizes medically relevant topics to support efficient data collection and reduce off-topic discussion. For safety, trained human monitors oversee conversations in real time and can intervene if risks such as self-harm arise. The AI intake is compared with patients' experiences with their clinicians through monthly follow-up questionnaires over four months. The study evaluates patients' ratings of empathy, communication quality, and engagement, not conversation content. Each participant serves as their own control, with AI intake and usual care compared within-subject and randomized by order of exposure.

Study Details

The goal of this clinical trial is to evaluate how a conversational method of collecting medical history affects patients' perceptions and experiences compared to clinical care as usual. This conversational AI intake system collects medical history information, can be completed by participants at home, and do not disrupt routine clinical care. The primary questions this study aims to answer are: 1\) Does conversational intake affect patients' perceptions of empathy during their clinical interactions? This will be a prospective study that follows a cohort of participants for four (4) months after engaging with the AI intake system. Because each participant serves as his/her own control, both comparators will be administered within-subject, and the order of exposure (AI intake vs. usual care) will be randomized to minimize sequence effects. After completing the AI intake method, participants will rate their experience, particularly in terms of empathy and compare it to their usual interactions with their own clinicians.

Key Dates

Start date
Apr 15, 2026
Status verified
Jul 2025
Primary completion
Apr 15, 2028
Completion
Jun 30, 2028

Study Design

Enrollment
130 participants (estimated)
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
HEALTH_SERVICES_RESEARCH

Arms

  • Experimental: Conversational AI system vs Usual Care
    Participants complete medical history intake through an interactive conversational AI designed to support patient-centered, empathetic dialogue. Using large language models (LLM), the system interprets patient input, maintains context, and generates natural-language responses. A dialogue manager prioritizes medically relevant topics to support efficient data collection and reduce off-topic discussion. For safety, trained human monitors oversee conversations in real time and can intervene if risks such as self-harm arise. The AI intake is compared with patients' experiences with their clinicians through monthly follow-up questionnaires over four months. The study evaluates patients' ratings of empathy, communication quality, and engagement, not conversation content. Each participant serves as their own control, with AI intake and usual care compared within-subject and randomized by order of exposure.

Primary Outcome Measure

Perceptions of empathy [ Time Frame: From enrollment up to 4 months after participation ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
George Mason UniversityFairfaxVirginia22030
Farrokh Alemi, PhD
[email protected]
7579459484
Kevin Lybarger, PhD
[email protected]
7579459484
Farrokh Alemi, PhD (PRINCIPAL_INVESTIGATOR)
Kevin Lybarger, PhD (PRINCIPAL_INVESTIGATOR)

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