Stella Namuganza

Definition of the problem In this article, a review of neuroethics is provided. Building on an earlier paper by Müller et al. (2018), we identify continuities and shed light on more recent important developments. Arguments In particular, we show that neuroethics is partly confronted with new substantive challenges. In addition, the question of how to deal with diversity plays a growing role: neuroethics is increasingly reflecting on the extent to which certain social groups are under- or overrepresented in research and ethical discourse, as well as the resulting blind spots. In this context, adopting a global perspective that takes cultural plurality into account is becoming increasingly important for neuroethics. Finally, we point out existing and emerging forms of institutionalization and networking with which the scientific community is responding to these challenges. Conclusion We argue that neuroethics should focus more on inclusive and participatory research concepts.

Artificial Intelligence (AI) presents unprecedented opportunities to transform healthcare worldwide, from improving diagnostic accuracy to expanding access in underserved regions. Despite this potential and growing investment, a significant gap persists between AI’s theoretical promise and its realised benefits in healthcare settings. This article examines the complex barriers impeding AI benefits realization in global health contexts, including ethical uncertainties, data infrastructure limitations, evidence quality concerns, and regulatory ambiguities. We analyze current initiatives addressing these challenges and highlight how technological solutions alone cannot resolve fundamental healthcare inequities. Drawing on the interdisciplinary perspectives and insights presented at the Global Health in the Age of AI Symposium hosted by the Cini Foundation and Yale Digital Ethics Center, we propose five core infrastructure requirements necessary for ethical AI implementation: robust data exchange; epistemic certainty with staff autonomy; actively protected healthcare values; validated outcomes with meaningful accountability; and environmental sustainability. These requirements form the foundation for a systems approach that balances technological advancement with ethical imperatives, contextual adaptability, and global equity considerations. We conclude that the successful integration of AI into healthcare demands coordinated action across sectors and borders, with careful attention to avoiding technological colonialism and ensuring AI serves as a force for health equity rather than widening existing disparities.