Using AI to Train Future Clinicians in Depression Assessment: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Depression is a major global health care challenge, causing significant individual distress but also contributing to a substantial global burden. Timely and accurate diagnosis is crucial. To help future clinicians develop these essential skills, we trained a generative pretrained transformer (GPT)-powered chatbot to simulate patients with varying degrees of depression and suicidality. OBJECTIVE: This study aims to evaluate the applicability and transferability of our GPT-4-powered chatbot for psychosomatic cases. Specifically, we aim to investigate how accurately the chatbot can simulate patients exhibiting various stages of depression and phases of suicidal ideation, while adhering to a predefined role script and maintaining a sufficient level of authenticity. Additionally, we want to analyze to what level the chatbot is suitable for practicing correctly diagnosing depressive disorders in patients, as well as assessing suicidality stages. METHODS: We developed 3 virtual patient role scripts depicting complex, realistic cases of depression and varying degrees of suicidality collaboratively with field experts and aligned with mental health assessment guidelines. These cases were integrated into a GPT-4-powered chatbot for practicing clinical history-taking. A total of 148 medical students, with an average age of 22.71 years and mostly in their sixth semester, interacted individually with one of the randomly assigned virtual patients through chat. Following this, they completed a questionnaire assessing their demographics and user experience. Chats were analyzed descriptively to assess diagnostic accuracy and suicidality assessments, as well as the role script adherence and authenticity of the artificial intelligence (AI). This was done to gain further insight into the chatbot's behavior and the students' diagnostic accuracy. RESULTS: In over 90% (725/778) of the answers, the chatbot maintained its assigned role. On average, students correctly identified the severity of depression in 60% (81/135) and the phase of suicidality in 67% (91/135) of the cases. Notably, the majority either failed to address or insufficiently explored the topic of suicidality despite explicit instructions beforehand. CONCLUSIONS: This study demonstrates that a GPT-powered chatbot can simulate patients with depression fairly accurately. More than two-thirds of participants perceived the AI-simulated patients with depression as authentic, and nearly 80% (106/135) indicated they would like to use the application for further practice, highlighting its potential as a training tool. While a small proportion of students expressed reservations, and the overall diagnostic accuracy varied depending on the severity of the case, the findings overall support the feasibility and educational value of AI-based role-playing in clinical training. AI-supported virtual patients provide a highly flexible, standardized, and readily available training tool, independent of real-life constraints.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".