Artificial Intelligence as a Structural Mediator of Person-Centred Nursing Care: A Theoretical-Reflective Analysis Using the Fundamentals of Care Framework.

AIM: To critically analyse how the integration of artificial intelligence into clinical nursing practice reconfigures the foundations of person-centred care, in light of the Fundamentals of Care Framework.

METHODS

This is a theoretical-reflective study using the relational, integrative and contextual dimensions of the Fundamentals of Care Framework as analytical axes.

DISCUSSION: The analysis argues that the integration of artificial intelligence modifies the conditions under which person-centred care is delivered. In the relational dimension, technological mediation redistributes professional attention and influences clinical decision-making. In the integrative dimension, the expansion of monitoring and coordination does not, by itself, ensure coherent articulation among physical, psychosocial and relational needs. In the contextual dimension, the adoption of this technology is conditioned by organizational, political and regulatory factors that shape care priorities and quality criteria.

CONCLUSION

Artificial intelligence does not replace care but transforms the structural conditions under which person-centred fundamental care is delivered. The quality of care remains dependent on the nurse's professional mediation, responsible for integrating digital data with lived experience and sustaining the relational, integrative and ethical commitment that underpins nursing practice.

IMPLICATIONS FOR THE PROFESSION: The integration of artificial intelligence requires relational competence, critical judgement and the ability to calibrate trust in algorithmic recommendations in light of the person's circumstances. Nursing organizations and leadership must ensure that digital transformation remains aligned with the principles of person-centred care, incorporating ethical governance, data protection and evaluation sensitive to patient experience. Professional education should prepare nurses to compare algorithmic outputs with patient narratives and their own clinical assessments, and to sustain the care relationship across varying levels of cognition, communication and cultural reference.