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Virtual patients, real conversations: ChatGPT advanced voice mode for pain communication training.

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Title: Virtual patients, real conversations: ChatGPT advanced voice mode for pain communication training.
Authors: Helms, Jeb T.1 (AUTHOR) jeb.helms@nau.edu, Crouch, Taylor B.2 (AUTHOR)
Source: Medical Teacher. Mar2026, Vol. 48 Issue 3, p357-359. 3p.
Subject Terms: *School environment, *Curriculum, *Role playing, *Artificial intelligence, *College teachers, *Communication education, *Health education, *Physical therapy education, Chronic pain treatment, Motivational interviewing, Medical specialties & specialists, Privacy, Neurosciences, Simulated patients, Physician-patient relations, Pain management, Physicians, Chatbots, Physical therapy students, Medical ethics, Ethics
Geographic Terms: United States
Abstract: What is the educational challenge? Across many healthcare disciplines, clinicians report feeling unprepared to treat and communicate effectively with patients presenting with chronic pain. In-person role-playing activities are resource-intensive, relying on multiple faculty or classmates who cannot provide feedback at the same depth as faculty. What are the proposed solutions? ChatGPT's Advanced Voice Mode (AVM) offers a novel solution. Using specific prompts, AVM enables learners to engage in real-time voice conversations with simulated patients, helping to build confidence in evidence-based communication tools like motivational interviewing. When piloted in a physical therapy class, learner feedback was generally positive, but some shortcomings and suggestions for improvement were also noted. What are the potential benefits to a wider global audience? By offering personalized feedback, AVM allows learners to practice skills outside of class time. This approach offers a scalable way to deliver individualized communication feedback in health education setting with large cohorts, and limited faculty member or simulation lab resources. What are the next steps? Further research is needed to validate the fidelity and effectiveness of AVM feedback and its more widespread feasibility as an assessment tool. Addressing learner privacy concerns and refining prompts to ensure realistic patient interactions will be crucial for broader implementation. [ABSTRACT FROM AUTHOR]
Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: What is the educational challenge? Across many healthcare disciplines, clinicians report feeling unprepared to treat and communicate effectively with patients presenting with chronic pain. In-person role-playing activities are resource-intensive, relying on multiple faculty or classmates who cannot provide feedback at the same depth as faculty. What are the proposed solutions? ChatGPT's Advanced Voice Mode (AVM) offers a novel solution. Using specific prompts, AVM enables learners to engage in real-time voice conversations with simulated patients, helping to build confidence in evidence-based communication tools like motivational interviewing. When piloted in a physical therapy class, learner feedback was generally positive, but some shortcomings and suggestions for improvement were also noted. What are the potential benefits to a wider global audience? By offering personalized feedback, AVM allows learners to practice skills outside of class time. This approach offers a scalable way to deliver individualized communication feedback in health education setting with large cohorts, and limited faculty member or simulation lab resources. What are the next steps? Further research is needed to validate the fidelity and effectiveness of AVM feedback and its more widespread feasibility as an assessment tool. Addressing learner privacy concerns and refining prompts to ensure realistic patient interactions will be crucial for broader implementation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=191766296
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      – Type: doi
        Value: 10.1080/0142159X.2025.2536149
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        Text: English
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      – SubjectFull: Role playing
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      – SubjectFull: Physical therapy education
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      – SubjectFull: Chronic pain treatment
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      – SubjectFull: Physicians
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      – SubjectFull: Chatbots
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      – SubjectFull: Physical therapy students
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      – SubjectFull: United States
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              Text: Mar2026
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