AI-Mediated Relational Competence in Medical Education: Construct Rationale and Curriculum Maturity Framework.
- 1Department of Health Management and Policy, College of Health and Human Services, University of New Hampshire, 4 Library Lane, Durham, NH, 03824, United States, 1 6038625047.
Abstract
Unlabelled
AI is quickly becoming a foundational component of many clinical workflows. New tools are continually evolving to enable a host of functionalities, from ambient documentation to enhanced decision support, assistive diagnostic tools, risk prediction, patient triage, and patient communication. As a result, medical education has begun to address issues of AI literacy and the ethics of its use, including some work on competency frameworks, yet comparatively little attention has been given to the relational competencies clinicians require when AI increasingly mediates the patient encounter. Existing AI competency frameworks emphasize technical literacy, evaluation skills, and sometimes ethical awareness, yet the literature suggests that AI tools alter the structure of the patient-clinician relationship itself, raising questions about trust, transparency, empathic presence, and accountable human judgment. The empathy education literature has established that empathic communication is both teachable and assessable, and the AI competency literature has established that clinicians require new knowledge and skills for AI-enabled practice. These 2 bodies of work, however, remain insufficiently integrated. Medical education currently lacks a coherent competency framework for the relational work clinicians must perform when AI tools shape clinical information, communication, documentation, diagnosis, risk prediction, or treatment recommendations. This viewpoint proposes AI-mediated relational competence as a distinct construct for medical education, operationalized through 6 observable subcompetencies: empathic presence, trustworthiness, transparency in AI use, relational judgment, accountable human decision-making, and equity-aware trust-building. A complementary 5-level curriculum maturity model is introduced for evaluating whether medical schools move beyond curricular mention to systematic, competency-based implementation. Together, the construct and maturity framework are intended to provide the conceptual foundation for subsequent empirical work, including online curriculum assessment, national survey research, expert consensus validation, and competency-based assessment instrument development.
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BibTeX
@article{mcgrath2026aimediated,
title = {{AI-Mediated Relational Competence in Medical Education: Construct Rationale and Curriculum Maturity Framework.}},
author = {Robert J McGrath},
journal = {JMIR medical education},
year = {2026},
month = oct,
doi = {10.2196/105459},
url = {https://europepmc.org/article/MED/42954149},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI-Mediated Relational Competence in Medical Education: Construct Rationale and Curriculum Maturity Framework.
AU - Robert J McGrath
PY - 2026
DA - 2026/10/27
JO - JMIR medical education
DO - 10.2196/105459
UR - https://europepmc.org/article/MED/42954149
AB - Unlabelled AI is quickly becoming a foundational component of many clinical workflows. New tools are continually evolving to enable a host of functionalities, from ambient documentation to enhanced decision support, assistive diagnostic tools, risk prediction, patient triage, and patient communication. As a result, medical education has begun to address issues of AI literacy and the ethics of its use, including some work on competency frameworks, yet comparatively little attention has been given to the relational competencies clinicians require when AI increasingly mediates the patient encounter. Existing AI competency frameworks emphasize technical literacy, evaluation skills, and sometimes ethical awareness, yet the literature suggests that AI tools alter the structure of the patient-clinician relationship itself, raising questions about trust, transparency, empathic presence, and accountable human judgment. The empathy education literature has established that empathic communication is both teachable and assessable, and the AI competency literature has established that clinicians require new knowledge and skills for AI-enabled practice. These 2 bodies of work, however, remain insufficiently integrated. Medical education currently lacks a coherent competency framework for the relational work clinicians must perform when AI tools shape clinical information, communication, documentation, diagnosis, risk prediction, or treatment recommendations. This viewpoint proposes AI-mediated relational competence as a distinct construct for medical education, operationalized through 6 observable subcompetencies: empathic presence, trustworthiness, transparency in AI use, relational judgment, accountable human decision-making, and equity-aware trust-building. A complementary 5-level curriculum maturity model is introduced for evaluating whether medical schools move beyond curricular mention to systematic, competency-based implementation. Together, the construct and maturity framework are intended to provide the conceptual foundation for subsequent empirical work, including online curriculum assessment, national survey research, expert consensus validation, and competency-based assessment instrument development.
SN - 2369-3762
ER -