Recruiting

Conversational AI

Sponsor:

George Mason University

Code:

NCT07105397

Conditions

Major Depressive Disorder (MDD)

Eligibility Criteria

Sex: All

Age: 18 - 70+

Healthy Volunteers: Not accepted

Interventions

Conversational AI system vs Usual Care

Study Details

Brief summary:

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.

Conditions

Major Depressive Disorder (MDD)

Study ID

NCT07105397

Start date

Jul 22, 2026

Status verified date

Sep, 2026

Completion date

Jun 30, 2028

Anticipated

Primary completion date

Apr 15, 2028

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18 - 70+

Healthy Volunteers: Not accepted

1. Participant is between 18 to 85 years old.
2. Participant has been, or are likely to be, diagnosed with moderate to severe Major Depressive Disorder without signs of bipolar depression.
3. Participant is not in active suicidal crisis and do not face imminent risk of suicide within the next 3 hours.
4. Participant is not pregnant or seeking to be pregnant.
5. Participant able to communicate in English on the Internet.
6. Participant must reside in the United States.
7. Participant has access to a mental health clinician, or the participant is willing to see study clinicians to help review the advice and medication adjustments recommended by the AI Intake System to the participant.
8. If the participant is seeing study clinicians, the participant must reside in a state where study clinicians are licensed.
9. Participant must be using a device (phone or computer) that is located in the United States.

Study Design

Enrollment

130 participants

Anticipated

Intervention Model

Single group

Primary purpose

Health Services Research

Interventions and Outcome Measures

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.

Interventions

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 and Locations

Central contacts

Farrokh Alemi, PhD

7579459484rapidai@gmu.edu

Kevin Lybarger, PhD

7579459484rapidai@gmu.edu

Locations

George Mason University

Recruiting

Fairfax, Virginia, United States, 22030

Contacts

Farrokh Alemi, PhD

7579459484rapidai@gmu.edu

Kevin Lybarger, PhD

7579459484rapidai@gmu.edu

Principal Investigator:

Farrokh Alemi, PhD

More Information

Sponsor

George Mason University

Last update posted

Sep 9, 2026

Last verified

Sep, 2026

Keywords

  • major depression
  • antidepressant
  • large language model
  • conversational agent
  • artificial intelligence
  • AI
  • NLP
  • major depressive disorder
  • medical record augmented generation
  • patient simulation
  • testbed

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by George Mason University on 2026-09-09.