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The role of artificial intelligence in enhancing interprofessional education and collaborative practice: a mixed methods scoping review.

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Title: The role of artificial intelligence in enhancing interprofessional education and collaborative practice: a mixed methods scoping review.
Authors: Hu, Lucas (AUTHOR), Argus, Geoff (AUTHOR), Pineda, Roi Charles (AUTHOR), MacAskill, William (AUTHOR), Martin, Priya (AUTHOR)
Source: Journal of Interprofessional Care. Mar/Apr2026, Vol. 40 Issue 2, p390-399. 10p.
Subjects: Interdisciplinary education, Documentation, Data security, Work, Medical information storage & retrieval systems, Risk assessment, Interprofessional relations, Data management, Computer software, Artificial intelligence, Educational outcomes, Health occupations students, Clinical decision support systems, Research evaluation, CINAHL database, Social role, Systematic reviews, Patient-centered care, Students, MEDLINE, Simulation methods in education, Surveys, Virtual reality, Communication, Intensive care units, Electronic health records, Trust, Social skills, Medical databases, Length of stay in hospitals, Machine learning, Individualized medicine, Honesty, Online information services, Health care teams, Patients' attitudes, Chatbots, Algorithms, Psychology information storage & retrieval systems, Evaluation
Abstract: In this scoping review, we examined the role of artificial intelligence (AI) in enhancing interprofessional education and collaborative practice (IPECP) within healthcare settings. Drawing on the Canadian Interprofessional Health Collaborative "Competency Framework," the review investigated AI's capacity to support essential IPECP competencies, including team communication, relationship-focused care, role clarification, and collaborative leadership. A comprehensive literature search identified 15 studies published from 2010 onwards that explored various AI applications, such as virtual reality simulations, clinical decision support systems, and machine learning algorithms, aimed at fostering interprofessional teamwork and improving healthcare outcomes. Key findings suggest that AI could facilitate effective team communication, real-time decision-making, and interprofessional education by enabling consistent, evidence-based recommendations and personalized treatment plans. However, several barriers to AI adoption were noted, including clinician mistrust, data security concerns, and challenges integrating AI within existing healthcare infrastructure. These findings highlight the potential for AI to advance IPECP but underscore the need for further research explicitly aligned with targeted IPECP competencies. Addressing these barriers will be critical to integrating AI into standard team-based healthcare practices. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Interprofessional Care 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: Psychology and Behavioral Sciences Collection
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  Data: In this scoping review, we examined the role of artificial intelligence (AI) in enhancing interprofessional education and collaborative practice (IPECP) within healthcare settings. Drawing on the Canadian Interprofessional Health Collaborative "Competency Framework," the review investigated AI's capacity to support essential IPECP competencies, including team communication, relationship-focused care, role clarification, and collaborative leadership. A comprehensive literature search identified 15 studies published from 2010 onwards that explored various AI applications, such as virtual reality simulations, clinical decision support systems, and machine learning algorithms, aimed at fostering interprofessional teamwork and improving healthcare outcomes. Key findings suggest that AI could facilitate effective team communication, real-time decision-making, and interprofessional education by enabling consistent, evidence-based recommendations and personalized treatment plans. However, several barriers to AI adoption were noted, including clinician mistrust, data security concerns, and challenges integrating AI within existing healthcare infrastructure. These findings highlight the potential for AI to advance IPECP but underscore the need for further research explicitly aligned with targeted IPECP competencies. Addressing these barriers will be critical to integrating AI into standard team-based healthcare practices. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Interprofessional Care 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.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/13561820.2025.2576241
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 390
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      – SubjectFull: Interdisciplinary education
        Type: general
      – SubjectFull: Documentation
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      – SubjectFull: Data security
        Type: general
      – SubjectFull: Work
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      – SubjectFull: Medical information storage & retrieval systems
        Type: general
      – SubjectFull: Risk assessment
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      – SubjectFull: Interprofessional relations
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      – SubjectFull: Computer software
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Educational outcomes
        Type: general
      – SubjectFull: Health occupations students
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      – SubjectFull: Clinical decision support systems
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      – SubjectFull: Research evaluation
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      – SubjectFull: CINAHL database
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      – SubjectFull: Medical databases
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      – SubjectFull: Length of stay in hospitals
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      – SubjectFull: Machine learning
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      – SubjectFull: Individualized medicine
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      – SubjectFull: Honesty
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      – SubjectFull: Algorithms
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      – SubjectFull: Psychology information storage & retrieval systems
        Type: general
      – SubjectFull: Evaluation
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      – TitleFull: The role of artificial intelligence in enhancing interprofessional education and collaborative practice: a mixed methods scoping review.
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              Text: Mar/Apr2026
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