Enabling equitable global health AI with privacy‑enhancing technologies

The global AI divide in healthcare is widening, with low-resource settings underrepresented in training data and underserved by infrastructure. Privacy-enhancing technologies (PETs) offer a path forward by enabling collaborative model development without centralizing sensitive data, but many remain difficult to implement in resource-constrained settings. In this Perspective, we examine the PET landscape, assess feasibility, address emerging challenges with large language models, and propose four priorities for equitable implementation.