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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.00709 |
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| _version_ | 1866914222842052608 |
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| author | Botero, Veronica Bossio Yadav, Vijay Ouyang, Jacob Abbas, Anzar Worthington, Michelle |
| author_facet | Botero, Veronica Bossio Yadav, Vijay Ouyang, Jacob Abbas, Anzar Worthington, Michelle |
| contents | Training mental health clinicians to conduct standardized clinical assessments is challenging due to a lack of scalable, realistic practice opportunities, which can impact data quality in clinical trials. To address this gap, we introduce a voice-enabled virtual patient simulation system powered by a large language model (LLM). This study describes the system's development and validates its ability to generate virtual patients who accurately adhere to pre-defined clinical profiles, maintain coherent narratives, and produce realistic dialogue. We implemented a system using a LLM to simulate patients with specified symptom profiles, demographics, and communication styles. The system was evaluated by 5 experienced clinical raters who conducted 20 simulated structured MADRS interviews across 4 virtual patient personas. The virtual patients demonstrated strong adherence to their clinical profiles, with a mean item difference between rater-assigned MADRS scores and configured scores of 0.52 (SD=0.75). Inter-rater reliability across items was 0.90 (95% CI=0.68-0.99). Expert raters consistently rated the qualitative realism and cohesiveness of the virtual patients favorably, giving average ratings between "Agree" and "Strongly Agree." Our findings suggest that LLM-powered virtual patient simulations are a viable and scalable tool for training clinicians, capable of producing high-fidelity, clinically relevant practice scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Voice-Enabled Virtual Patient System for Interactive Training in Standardized Clinical Assessment Botero, Veronica Bossio Yadav, Vijay Ouyang, Jacob Abbas, Anzar Worthington, Michelle Human-Computer Interaction Artificial Intelligence Training mental health clinicians to conduct standardized clinical assessments is challenging due to a lack of scalable, realistic practice opportunities, which can impact data quality in clinical trials. To address this gap, we introduce a voice-enabled virtual patient simulation system powered by a large language model (LLM). This study describes the system's development and validates its ability to generate virtual patients who accurately adhere to pre-defined clinical profiles, maintain coherent narratives, and produce realistic dialogue. We implemented a system using a LLM to simulate patients with specified symptom profiles, demographics, and communication styles. The system was evaluated by 5 experienced clinical raters who conducted 20 simulated structured MADRS interviews across 4 virtual patient personas. The virtual patients demonstrated strong adherence to their clinical profiles, with a mean item difference between rater-assigned MADRS scores and configured scores of 0.52 (SD=0.75). Inter-rater reliability across items was 0.90 (95% CI=0.68-0.99). Expert raters consistently rated the qualitative realism and cohesiveness of the virtual patients favorably, giving average ratings between "Agree" and "Strongly Agree." Our findings suggest that LLM-powered virtual patient simulations are a viable and scalable tool for training clinicians, capable of producing high-fidelity, clinically relevant practice scenarios. |
| title | A Voice-Enabled Virtual Patient System for Interactive Training in Standardized Clinical Assessment |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2511.00709 |