Evaluating Conversational Artificial Intelligence for Depression Management
ConditionMajor Depressive Disorder (MDD)
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18-85
SponsorGeorge Mason University
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Participant is between 18 to 85 years old.
Participant has been, or are likely to be, diagnosed with moderate to severe Major Depressive Disorder without signs of bipolar depression.
Participant is not in active suicidal crisis and do not face imminent risk of suicide within the next 3 hours.
Participant is not pregnant or seeking to be pregnant.
Disqualifiers
None
Trial design
Design model
Single group
Treatments tested in this trial
Conversational AI system vs Usual Care
Other interventionParticipants 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.
Treatment groups
130 Participants
are divided into 1 treatment groupGroup A: Conversational AI system vs Usual CareExperimental treatment 1 intervention
Trial outcomes
Primary outcomes
1
Perceptions of empathy
The primary outcome assesses the impact of intake methods (conversational AI vs. structured survey) on patients' perceptions of empathy. Patient empathy perceptions will be measured using the Jefferson Scale of Empathy (JSE), a validated instrument. A higher JSE score means higher perceptions of empathy. We hypothesize that patients interacting with the conversational AI will report higher perceived empathy scores compared to those using the structured survey. For evaluation of impact of comparators, the primary analysis will focus on impact of LLM and clinician encounters on patient perceived empathy. These data will be analyzed using paired comparisons (ANOVA), with randomized order of exposure accounted for in the design. At the power of 0.80, a small-to medium effect size (d = 0.3), and significance level of 0.05, a sample size of 90 participants is needed, with 112 participants needed with attrition. In order to support higher power, a total of 130 participants will be recruited.
Time frame
From enrollment up to 4 months after participationSecondary outcomes
1
Communication Accommodation
The study team will assess communication accommodation to determine whether the conversational AI meets patient needs through a content analysis of conversation transcripts. This analysis will rate three key dimensions: empathy, communication quality, and effectiveness. Codes for each are drawn from validated, widely used scales. From this, the study team will generate composite scores to classify AI conversations as high or low in each dimension and overall. To validate the findings, the study team will compare content analysis scores with patient self-reports on perceived levels (high or low) of empathy, quality, and effectiveness. Together, these data will provide a robust evaluation of the AI's communication accommodation.
Time frame
From enrollment up to 4 months after participation2
Adherence to recommendations
Adherence to the recommendations provided by the conversational AI will be evaluated approximately one month after patient participation. Follow-up includes a structured questionnaire and brief interview, where patients report (1) if they discussed system-generated recommendations with their clinician, and (2) their current medications by direct reference to medication containers. Collected data will allow classification of clinician prescriptions as either concordant or discordant with system-generated recommendations, facilitating evaluation of adherence behaviors.
Time frame
From enrollment up to 4 months after participationSponsors and contacts
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George Mason University
Lead sponsor
Patient-Centered Outcomes Research Institute
Collaborator
Source ClinicalTrials.gov