Major Depressive Disorder (MDD)NCT07105397
Evaluating Conversational Artificial Intelligence for Depression Management
A study of Major Depressive Disorder (MDD), run by George Mason University at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- Major Depressive Disorder (MDD)
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 130 participants
- Where
- 1 siteUnited States
- Study dates
- 22 Jul 2026to 30 Jun 2028
- 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 18 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
- 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.
- Participant able to communicate in English on the Internet.
- Participant must reside in the United States.
- 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.
- If the participant is seeing study clinicians, the participant must reside in a state where study clinicians are licensed.
- Participant must be using a device (phone or computer) that is located in the United States.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
George Mason University
Fairfax, Virginia, 22030
Farrokh Alemi, PhD, 7579459484, rapidai@gmu.edu
Kevin Lybarger, PhD, 7579459484, rapidai@gmu.edu
About this study
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:
Read more
- 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.
Read the detailed description
Conversational artificial intelligence (AI) systems, such as those based on Large Language Models (LLMs) like ChatGPT, offer innovative ways to engage patients in health-related conversations. Despite these advances, challenges remain regarding patient safety and system reliability. Specific concerns include biased recommendations against certain patient groups, inaccuracies or misleading responses, and mechanical, unempathic interactions, particularly during sensitive moments such as when patients express suicidal thoughts. Testing conversational AI in healthcare settings is complicated due to the diverse medical, linguistic, and behavioral characteristics exhibited by patients.
This study addresses these challenges by developing an advanced conversational AI system guided by a structured knowledge-based topic network to maintain conversation relevance and coherence. Additionally, the investigators introduce a novel patient simulator methodology that mimics diverse medical histories, linguistic styles, and behavioral interactions, enhancing pre-clinical testing rigor.
The research focuses specifically on the clinical context of depression management, aiming to optimize antidepressant selection. Currently, many patients undergo a frustrating and costly trial-and-error process to find effective antidepressants. The study compares two approaches and their impact on a patient's perceptions of empathy:
- Conversational AI Intake: Engages patients through flexible, open-ended dialogue to gather medical history and generate personalized antidepressant recommendations.
- Usual Care: Reflects the physician's clinical judgment about how to best treat the depressive disorder, including the severity level at which treatment is required.
The conversational AI intake system leverage a curated, evidence-based knowledgebase of 15 commonly used antidepressants, considering factors like patient age, gender, comorbidities, and previous antidepressant use. The accuracy and completeness of the AI-generated recommendations are rigorously verified in by clinicians prior to any medication changes, adhering to FDA safety requirements.
This will be a prospective study that follows a cohort of participants for four (4) months after engaging with the AI intake system. A primary goal of the project is to evaluate how conversational AI impacts patient-centered outcomes, specifically patient perceptions of empathy and communication quality. Patients with major depressive disorder will be recruited online, enhancing participant diversity and representativeness. 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. Outcomes will include differences in data completeness and patient perceptions of empathy. To ensure that the AI conversation is evaluated against the best of usual care, we will select the highest empathy score achieved across multiple visits as the usual care comparator.
Beyond immediate clinical outcomes, the project's methodological advancements, particularly the development of robust, bias-mitigated conversational systems and comprehensive patient simulation for AI testing, will have broad applicability across healthcare domains. The conversational AI and patient simulator will be made publicly available at no cost, providing tools that other researchers, clinicians, and healthcare providers can utilize and adapt to various health contexts.
Patient and stakeholder engagement is integral to the study. A representative advisory board, including patients with lived experience of depression, clinicians, mental health advocates, and researchers, guides all phases of the project. This collaborative framework ensures that the research remains patient-centered and responsive to real-world clinical needs and experiences.
- Conditions
- Major Depressive Disorder (MDD)
- Being tested
- Conversational AI system vs Usual Care
- Keywords
- major depression, antidepressant, large language model, conversational agent, artificial intelligence, AI, NLP, major depressive disorder, medical record augmented generation, patient simulation, testbed
- Medical subject headings
- Depressive Disorder, Major
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Conversational AI system vs Usual CareOther
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 group | Role | What they receive |
|---|---|---|
| Conversational AI system vs Usual Care | Experimental | 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. Receives: Other: Conversational AI system vs Usual Care |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: From enrollment up to 4 months after participation
Secondary outcomes (2)
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.
Measured: From enrollment up to 4 months after participation
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.
Measured: From enrollment up to 4 months after participation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Aug 2025
- Study start22 Jul 2026
- Primary completion15 Apr 2028 (estimated)
- Study completion30 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Major Depressive Disorder (MDD)Studies of the same condition, open and under way.
- 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
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- This will be a prospective study that follows a cohort of participants for four (4) months after engaging with the AI intake system. Each subject 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.
- Masking
- None (open label)
- Enrolment
- 130 participants (estimated)
Sponsor and investigators
- Lead sponsor
- George Mason University
- Sponsor type
- University, hospital or other
- Collaborators
- Patient-Centered Outcomes Research Institute
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07105397
- Sponsor's protocol ID
- STUDY00000316
- Other IDs
- ME-2024C1-36732
- Status last verified
- Sep 2026
- First submitted
- 1 Jul 2025
- Last updated
- 9 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified individual participant data from this study will be shared with the Patient-Centered Outcomes Research Institute (PCORI) and made available through PCORI's designated data repository. Qualified researchers may request access to the data by following PCORI's established data access procedures, which include submission of a research proposal and a signed data use agreement.
Study at a glance
From the registry record, last updated by the sponsor on 9 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating Conversational Artificial Intelligence for Depression Management
- ClinicalTrials.gov ID
- NCT07105397
- Acronym / sponsor ID
- STUDY00000316
- Conditions
- Major Depressive Disorder (MDD)
- Keywords
- major depression, antidepressant, large language model, conversational agent, artificial intelligence, AI, NLP, major depressive disorder
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 130 participants (estimated)
- Lead sponsor
- George Mason UniversityUniversity, hospital or other
- Collaborators
- Patient-Centered Outcomes Research Institute
- Study start
- 22 Jul 2026 (actual)
- Primary completion
- 15 Apr 2028 (estimated)
- Study completion
- 30 Jun 2028 (estimated)
- First posted
- 5 Aug 2025 (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.
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:
- 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.
Detailed description
Conversational artificial intelligence (AI) systems, such as those based on Large Language Models (LLMs) like ChatGPT, offer innovative ways to engage patients in health-related conversations. Despite these advances, challenges remain regarding patient safety and system reliability. Specific concerns include biased recommendations against certain patient groups, inaccuracies or misleading responses, and mechanical, unempathic interactions, particularly during sensitive moments such as when patients express suicidal thoughts. Testing conversational AI in healthcare settings is complicated due to the diverse medical, linguistic, and behavioral characteristics exhibited by patients.
This study addresses these challenges by developing an advanced conversational AI system guided by a structured knowledge-based topic network to maintain conversation relevance and coherence. Additionally, the investigators introduce a novel patient simulator methodology that mimics diverse medical histories, linguistic styles, and behavioral interactions, enhancing pre-clinical testing rigor.
The research focuses specifically on the clinical context of depression management, aiming to optimize antidepressant selection. Currently, many patients undergo a frustrating and costly trial-and-error process to find effective antidepressants. The study compares two approaches and their impact on a patient's perceptions of empathy:
- Conversational AI Intake: Engages patients through flexible, open-ended dialogue to gather medical history and generate personalized antidepressant recommendations.
- Usual Care: Reflects the physician's clinical judgment about how to best treat the depressive disorder, including the severity level at which treatment is required.
The conversational AI intake system leverage a curated, evidence-based knowledgebase of 15 commonly used antidepressants, considering factors like patient age, gender, comorbidities, and previous antidepressant use. The accuracy and completeness of the AI-generated recommendations are rigorously verified in by clinicians prior to any medication changes, adhering to FDA safety requirements.
This will be a prospective study that follows a cohort of participants for four (4) months after engaging with the AI intake system. A primary goal of the project is to evaluate how conversational AI impacts patient-centered outcomes, specifically patient perceptions of empathy and communication quality. Patients with major depressive disorder will be recruited online, enhancing participant diversity and representativeness. 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. Outcomes will include differences in data completeness and patient perceptions of empathy. To ensure that the AI conversation is evaluated against the best of usual care, we will select the highest empathy score achieved across multiple visits as the usual care comparator.
Beyond immediate clinical outcomes, the project's methodological advancements, particularly the development of robust, bias-mitigated conversational systems and comprehensive patient simulation for AI testing, will have broad applicability across healthcare domains. The conversational AI and patient simulator will be made publicly available at no cost, providing tools that other researchers, clinicians, and healthcare providers can utilize and adapt to various health contexts.
Patient and stakeholder engagement is integral to the study. A representative advisory board, including patients with lived experience of depression, clinicians, mental health advocates, and researchers, guides all phases of the project. This collaborative framework ensures that the research remains patient-centered and responsive to real-world clinical needs and experiences.
Study design
- Primary purpose
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- This will be a prospective study that follows a cohort of participants for four (4) months after engaging with the AI intake system. Each subject 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.
| Arm | Type | Receives |
|---|---|---|
| Conversational AI system vs Usual Care | Experimental | Other: Conversational AI system vs Usual CareParticipants 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. |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
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 |
|---|---|---|
| Conversational AI system vs Usual CareParticipants 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. | Other | Conversational AI system vs Usual Care |
Primary outcome
Perceptions of empathy
Time frame: From enrollment up to 4 months after participation
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.
2 secondary outcomes
Communication Accommodation
Time frame: From enrollment up to 4 months after participation
Adherence to recommendations
Time frame: From enrollment up to 4 months after participation
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 9 Sep 2026.
- Individual participant dataSharing planned: Yes. De-identified individual participant data from this study will be shared with the Patient-Centered Outcomes Research Institute (PCORI) and made available through PCORI's designated data repository. Qualified researchers may request access to the data by following PCORI's established data access procedures, which include submission of a research proposal and a signed data use agreement.
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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 of Major Depressive Disorder (MDD), run by George Mason University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
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: looking for participants now, as of the sponsor's last update on 9 Sep 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 United States. 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 Farrokh Alemi, PhD (7579459484, rapidai@gmu.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Perceptions of empathy (time frame: From enrollment up to 4 months after participation). 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 9 Sep 2026.