Artificial intelligence is transforming diabetic retinopathy screening by allowing faster, more scalable, and more accurate diagnosis of retinal disease. However, this study by Chauhan et al. shows that the success of these systems depends not only on technical accuracy but also on trust. Through interviews with ophthalmologists, AI developers, bioethicists, and legal experts, the researchers identified six major themes that influence perceived trustworthiness. These were algorithmic performance, responsible data use, ethical oversight, transparency, implementation, and accountability. Many participants raised concerns about opaque data practices and insufficient patient consent, especially in low and middle income countries with weaker governance mechanisms. They emphasized that patients often remain unaware of how their retinal images are stored or shared, raising issues of privacy and ownership. The study also found that a lack of explainability in AI outputs reduces clinical confidence and public trust. These challenges risk data colonialism, where global health technologies extract sensitive data from under resourced systems without equal benefits. The authors call for stronger regulatory and consent frameworks, and greater public engagement in AI driven healthcare.