Over 10% of the global population is affected by chronic kidney disease (CKD) and those without preventative care and early intervention are the worst impacted. Many childhood precursors to CKD such as hydronephrosis (HN) continue to be detected and treated late in low- and middle-income countries where prenatal and early-life ultrasound is less common. Artificial intelligence-based technology holds promise for improving some of this detection and treatment. In this work, we explore the promise and pitfalls of transferring an AI-based tool for early HN detection in pediatric ultrasound from Canada, where it was developed and validated, to South Africa. We explore challenges and opportunities at the health-system-, institutional-, and provider-levels that using this technology in this new clinical environment involve. Our investigation is performed through interviews with clinicians at various levels, locations, and in different specialties. We find that the context of our tool's use will change in terms of both clinicians and patients, as the users of our tool in South Africa will have less access to pediatric sonography expertise and, for related reasons, patients will tend to be older when they receive an ultrasound imaging. These differences imply the need for adaptation of both the underlying algorithm as well as the tool's interface for successful deployment in this setting. However, given these revisions, the clinicians interviewed are eager for AI-based assistance in caring for these patients earlier and more effectively and believe algorithms of this kind will be useful for improving care.