JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is increasingly embedded in healthcare, yet its benefits remain unevenly distributed due to persistent concerns regarding bias, inequity, and socio-cultural misalignment. Although existing Ethical AI frameworks typically emphasize universal principles, they often insufficiently address the socio-cultural contexts in which AI systems are developed, implemented, and used.

Objective:

This systematic review aimed to examine how socio-cultural factors shape ethical challenges in healthcare AI, influence the interpretation of ethical principles, and inform context-sensitive design and governance strategies.

Methods:

Following PRISMA 2020 guidelines, we conducted a systematic search of PubMed, IEEE Xplore, and Web of Science for studies published between 2018 and 2025. Eligible studies addressed ethical issues related to AI in healthcare through a socio-cultural lens. A thematic synthesis combining inductive and deductive coding was used to analyze reported challenges, context-dependent ethical interpretations, and proposed mitigation approaches.

Results:

A total of 49 studies were included. The findings show that ethical challenges in healthcare AI are deeply embedded in structural inequalities, data collection, curation, and documentation practices, institutional conditions, and cultural norms rather than being purely technical problems. Key challenges included algorithmic bias, underrepresentation of minorities in datasets, cultural and linguistic mismatches, limited transparency and trust, and systemic disparities in access to AI technologies. The reviewed literature proposed a broad range of technical, design-related, and governance-oriented strategies, but these remained fragmented and were rarely integrated systematically across the AI lifecycle. Based on this synthesis, the study proposes the Inclusive Ethical AI Framework (IEAF), a socio-technical framework that systematically translates socio-cultural context into context-sensitive ethical interpretations and actionable design and governance decisions across the AI lifecycle.

Conclusions:

The findings highlight that ethical challenges in healthcare AI are fundamentally shaped by socio-cultural context and cannot be addressed through technical solutions or universal ethical principles alone. Instead, effective and equitable AI systems require the systematic integration of socio-cultural considerations into data practices, system design, and governance across the AI lifecycle. Clinical Trial: PROSPERO CRD420251058607; prospectively registered.