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The AWARE (AI Use, Why, Attachment, Reality and Risk, and Effect on Functioning) Framework for Assessing Patients' Use of AI in Mental Health Care.

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Τίτλος: The AWARE (AI Use, Why, Attachment, Reality and Risk, and Effect on Functioning) Framework for Assessing Patients' Use of AI in Mental Health Care.
Συγγραφείς: Hudon A; Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.; Forensic psychiatry services, Department of Psychiatry, Institut universitaire en santé mentale de Montréal, Montreal, QC, Canada.; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Montreal, QC, Canada.; Department of Psychiatry, Institut national de psychiatrie légale Philippe-Pinel, Montreal, QC, Canada.; Centre de recherche en pédagogie de la santé, Université de Montréal, Montreal, QC, Canada., Bhat V; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.; Department of Psychiatry, AI for Mental Health, St. Michael's Hospital, Toronto, ON, Canada., Nunez JJ; Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada., Paquin V; Department of Psychiatry, McGill University, Montreal, QC, Canada.; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada., Malouin-Lachance A; Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.; Department of Psychiatry, Faculté de médecine, Université Laval, Québec, QC, Canada., Lespérance F; Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.; Département de psychiatrie, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada.; Centre de recherche du Centre hospitalier de l'Université de Montréal, Montreal, QC, Canada.
Πηγή: JMIR medical education [JMIR Med Educ] 2026 Sep 16; Vol. 12, pp. e108766. Date of Electronic Publication: 2026 Sep 16.
Τύπος έκδοσης: Journal Article; Research Support, Non-U.S. Gov't
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101684518 Publication Model: Electronic Cited Medium: Internet ISSN: 2369-3762 (Electronic) Linking ISSN: 23693762 NLM ISO Abbreviation: JMIR Med Educ Subsets: MEDLINE
Imprint Name(s): Original Publication: Toronto, ON : JMIR Publications, [2015]-
Ιατρικοί όροι (MeSH): Artificial Intelligence* , Mental Health Services*, Humans ; Generative Artificial Intelligence
Περίληψη: Conversational AI has rapidly become integrated into everyday life, with many individuals using large language model-based systems, such as ChatGPT, Gemini, and Claude, for education, productivity, health information, decision-making, emotional support, and companionship. As patients increasingly incorporate conversational AI into their cognitive, emotional, and social lives, these interactions may influence coping strategies, treatment engagement, clinical decision-making, and mental health outcomes. However, although medical education has increasingly emphasized AI literacy and the responsible use of generative AI by clinicians, comparatively little attention has been given to preparing health care professionals to assess patients' use of conversational AI during routine clinical encounters. This Viewpoint introduces the AWARE (AI use, why, attachment, reality and risk, and effect on functioning) framework, a practical educational framework designed to help mental health professionals systematically assess patients' use of conversational AI. Rather than functioning as a diagnostic instrument or psychometric scale, AWARE provides a structured approach to psychiatric interviewing across 5 clinically relevant domains: AI use, why, attachment, reality and risk, and effect on functioning. Together, these domains guide clinicians in exploring patterns of AI use, the motivations underlying engagement, the emotional significance of patient-AI interactions, potential influences on reality testing and clinical risk, and the overall impact of AI on psychological well-being, daily functioning, relationships, and recovery. The framework is intended to complement existing psychiatric interviewing practices by supporting comprehensive history taking, clinical reasoning, risk assessment, documentation, and learner education. We discuss the rationale for routinely asking patients about AI use, review emerging evidence regarding both the potential benefits and risks of conversational AI, and describe how AWARE may be incorporated into undergraduate, postgraduate, and continuing professional education through simulation, objective structured clinical examinations, workplace-based assessment, and clinical supervision. We also outline priorities for future research, including validation, implementation, educational evaluation, and cross-cultural adaptation. As conversational AI becomes increasingly embedded within patients' daily lives, clinicians require practical approaches to understanding its role in mental health and health care. The AWARE framework offers a structured educational starting point for integrating assessment of patient-AI interactions into routine psychiatric interviewing while supporting patient-centered, evidence-informed clinical practice and health professions education.
(©Alexandre Hudon, Venkat Bhat, John-Jose Nunez, Vincent Paquin, Amylie Malouin-Lachance, François Lespérance. Originally published in JMIR Medical Education (https://mededu.jmir.org), 16.09.2026.)
Contributed Indexing: Keywords: AI; ChatGPT; artificial intelligence; clinical assessment; conversational artificial intelligence; digital mental health; large language models; medical education; mental health; psychiatric interview; psychiatry
Entry Date(s): Date Created: 20260916 Date Completed: 20260916 Latest Revision: 20260921
Update Code: 20260921
DOI: 10.2196/108766
PMID: 42749279
Βάση Δεδομένων: MEDLINE
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  Data: The AWARE (AI Use, Why, Attachment, Reality and Risk, and Effect on Functioning) Framework for Assessing Patients' Use of AI in Mental Health Care.
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  Data: Conversational AI has rapidly become integrated into everyday life, with many individuals using large language model-based systems, such as ChatGPT, Gemini, and Claude, for education, productivity, health information, decision-making, emotional support, and companionship. As patients increasingly incorporate conversational AI into their cognitive, emotional, and social lives, these interactions may influence coping strategies, treatment engagement, clinical decision-making, and mental health outcomes. However, although medical education has increasingly emphasized AI literacy and the responsible use of generative AI by clinicians, comparatively little attention has been given to preparing health care professionals to assess patients' use of conversational AI during routine clinical encounters. This Viewpoint introduces the AWARE (AI use, why, attachment, reality and risk, and effect on functioning) framework, a practical educational framework designed to help mental health professionals systematically assess patients' use of conversational AI. Rather than functioning as a diagnostic instrument or psychometric scale, AWARE provides a structured approach to psychiatric interviewing across 5 clinically relevant domains: AI use, why, attachment, reality and risk, and effect on functioning. Together, these domains guide clinicians in exploring patterns of AI use, the motivations underlying engagement, the emotional significance of patient-AI interactions, potential influences on reality testing and clinical risk, and the overall impact of AI on psychological well-being, daily functioning, relationships, and recovery. The framework is intended to complement existing psychiatric interviewing practices by supporting comprehensive history taking, clinical reasoning, risk assessment, documentation, and learner education. We discuss the rationale for routinely asking patients about AI use, review emerging evidence regarding both the potential benefits and risks of conversational AI, and describe how AWARE may be incorporated into undergraduate, postgraduate, and continuing professional education through simulation, objective structured clinical examinations, workplace-based assessment, and clinical supervision. We also outline priorities for future research, including validation, implementation, educational evaluation, and cross-cultural adaptation. As conversational AI becomes increasingly embedded within patients' daily lives, clinicians require practical approaches to understanding its role in mental health and health care. The AWARE framework offers a structured educational starting point for integrating assessment of patient-AI interactions into routine psychiatric interviewing while supporting patient-centered, evidence-informed clinical practice and health professions education.<br /> (©Alexandre Hudon, Venkat Bhat, John-Jose Nunez, Vincent Paquin, Amylie Malouin-Lachance, François Lespérance. Originally published in JMIR Medical Education (https://mededu.jmir.org), 16.09.2026.)
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