Global push for AI in healthcare risks deepening inequality

Artificial intelligence (AI) is increasingly integrated into healthcare systems around the world, promising enhanced diagnostics, streamlined workflows, and improved patient outcomes. However, recent research highlights significant ethical and practical challenges when these technologies are deployed in low-resource or postcolonial settings. The study focusing on Benin, a West African country, reveals that AI tools developed primarily in Global North contexts often fail to align with local healthcare infrastructures, clinical routines, and cultural understandings of care. This misalignment risks exacerbating existing inequalities rather than alleviating them.

The core issue stems from the fact that many AI healthcare solutions are designed with assumptions and data derived from high-income countries. These systems may not account for local disease prevalence, resource availability, or healthcare worker expertise in lower-income regions. For example, diagnostic algorithms trained on datasets from Western populations might perform poorly when applied to patients in Benin, leading to misdiagnoses or inappropriate treatment recommendations. Furthermore, the introduction of AI can disrupt established moral economies of care, where relationships between patients and providers are deeply rooted in trust and community knowledge.

Infrastructure challenges also play a critical role. Many healthcare facilities in low-resource settings lack reliable electricity, internet connectivity, or the necessary hardware to support sophisticated AI tools. This technological gap means that even well-intentioned AI interventions may be impractical or unsustainable. Additionally, healthcare workers may not receive adequate training to effectively use these systems, limiting their potential benefits and possibly undermining confidence in the technology.

Beyond technical and infrastructural concerns, there are broader ethical implications. The deployment of AI in healthcare without meaningful local input can perpetuate forms of digital colonialism, where Global North entities impose solutions that do not reflect or respect local needs and values. This dynamic risks marginalizing local expertise and priorities, potentially eroding community trust in healthcare institutions. Moreover, reliance on AI could shift decision-making away from human caregivers, altering the nature of care and patient-provider relationships in ways that may not be culturally appropriate.

Addressing these challenges requires a more inclusive and context-sensitive approach to AI in healthcare. Developers and policymakers must engage with local stakeholders, including healthcare workers, patients, and community leaders, to co-design AI systems that are tailored to specific environments. Investments in infrastructure and training are essential to ensure that AI tools can be effectively integrated and maintained. Furthermore, ethical frameworks guiding AI deployment should emphasize equity, transparency, and respect for local knowledge and values.

In summary, while AI holds great promise for transforming healthcare globally, its expansion into low-resource and postcolonial settings must be approached with caution. Without deliberate efforts to bridge contextual divides and prioritize local agency, AI risks deepening existing inequalities and reshaping care in ways that may undermine its fundamental goals of improving health outcomes and equity.