Preserving Person‐Centred Fundamental Care in Human‐ AI Collaboration: A Scoping Review

AIM: To map and synthesize evidence on Human-AI Collaboration (HAIC) in nursing and community care contexts guided by the Fundamentals of Care (FoC) framework DESIGN: Scoping Review. DATA SOURCES: Multiple databases including PubMed, Web of Science, CINAHL and PsycArticles were searched for articles published between January 2020 and July 2025. METHODS: Studies involving care providers or recipients collaborating with AI in nursing or community care contexts were included. Hospital-based diagnostic and non-empirical studies were excluded. REPORTING METHOD: The JBI updated nine-step framework and PRISMA-ScR guidelines were followed. RESULTS: In total 1744 articles were retrieved, of which 26 were included in this review. Three configurations of the triadic caring relationship were identified: provider-mediated, joint engagement and recipient-facing configurations. The HAIC tasks concentrated on physical care, while the integration of physical, psychosocial, and relational care remained limited. Five nursing roles were identified within HAIC: proactive care coordinator, AI output validator, data curator, AI literacy facilitator and AI co-designer. Accountability and human oversight were the most frequently reported ethical considerations. CONCLUSION: Preserving person-centred fundamental care within HAIC requires AI-supported information to be combined with professional judgment and human support to address recipients' physical, psychosocial and relational needs. Nurses remain central to sustaining continuity and translating this information into individualized care. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses should be educated to contribute to AI co-design and facilitate the understanding of AI-supported care among recipients and families. These competencies are important for aligning AI with recipients' multidimensional needs, supporting informed participation, and maintaining professional oversight in nursing and community settings. IMPACT: The HAIC expands nurses' responsibilities across care coordination, AI output validation and professional oversight. These findings can inform nursing education and the integration of AI into care workflows to sustain continuity and preserve person-centred fundamental care. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. REGISTRATION: Protocol registered: doi: https://osf.io/35km7/.

Authors

Institutions

Publication Details

Journal
Journal of Advanced Nursing
Published
2026-09-18
DOI
https://doi.org/10.1111/jan.70757
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Preserving Person‐Centred Fundamental Care in Human‐ AI Collaboration: A Scoping Review

Yeon Joo Son, Suk‐Sun Kim, Boram Lim, Daeun Kim et al.
Journal of Advanced Nursing
Artificial Intelligence in Healthcare and Education
article

Preserving Person‐Centred Fundamental Care in Human‐ AI Collaboration: A Scoping Review

Yeon Joo Son, Suk‐Sun Kim, Boram Lim, Daeun Kim, Boyoung Kim
article en

Abstract

AIM: To map and synthesize evidence on Human-AI Collaboration (HAIC) in nursing and community care contexts guided by the Fundamentals of Care (FoC) framework DESIGN: Scoping Review. DATA SOURCES: Multiple databases including PubMed, Web of Science, CINAHL and PsycArticles were searched for articles published between January 2020 and July 2025. METHODS: Studies involving care providers or recipients collaborating with AI in nursing or community care contexts were included. Hospital-based diagnostic and non-empirical studies were excluded. REPORTING METHOD: The JBI updated nine-step framework and PRISMA-ScR guidelines were followed. RESULTS: In total 1744 articles were retrieved, of which 26 were included in this review. Three configurations of the triadic caring relationship were identified: provider-mediated, joint engagement and recipient-facing configurations. The HAIC tasks concentrated on physical care, while the integration of physical, psychosocial, and relational care remained limited. Five nursing roles were identified within HAIC: proactive care coordinator, AI output validator, data curator, AI literacy facilitator and AI co-designer. Accountability and human oversight were the most frequently reported ethical considerations. CONCLUSION: Preserving person-centred fundamental care within HAIC requires AI-supported information to be combined with professional judgment and human support to address recipients' physical, psychosocial and relational needs. Nurses remain central to sustaining continuity and translating this information into individualized care. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses should be educated to contribute to AI co-design and facilitate the understanding of AI-supported care among recipients and families. These competencies are important for aligning AI with recipients' multidimensional needs, supporting informed participation, and maintaining professional oversight in nursing and community settings. IMPACT: The HAIC expands nurses' responsibilities across care coordination, AI output validation and professional oversight. These findings can inform nursing education and the integration of AI into care workflows to sustain continuity and preserve person-centred fundamental care. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. REGISTRATION: Protocol registered: doi: https://osf.io/35km7/.

Journal of Advanced Nursing
Ewha Womans University (KR)
Quality Education
Openalex Percentile: Top 14%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.