AI can enhance public health practice, but it requires careful consideration of ethical implications. We propose a reason-based framework to guide AI co-design and use for public health. AI systems must be developed with public health expertise, lived experience insights, and human accountability to ensure responsible outcomes. We advocate for ethical principles to be embedded throughout the AI lifecycle

Research on responsible digital health innovation has typically focused on technical aspects such as the reliability and trustworthiness. More recently, work in responsible digital health innovation has started to recognize that, to address those concerns, stakeholder involvement is key. Aligning technological advancements with stakeholders’ needs requires deliberate and inclusive processes. Such processes

Winter et al.’s recent investigation, “A Comparison of Self-Reported COVID-19 Symptoms Between Android and iOS CoronaCheck App Users,” reveals differences in the demographics and COVID-19 symptoms reported by users of Android and iOS systems. These findings not only provide more information about the varied experiences of individuals during the COVID-19 pandemic but also suggest that conclusions reached

The integration of generative artificial intelligence (AI) tools into healthcare poses significant challenges concerning data privacy and governance. Drawing on the historical vignette of Henrietta Lacks, this perspective examines the implications of using generative AI in clinical settings. We discuss current health data governance practices and their potential limitations in the generative AI era

This perspective article examines the role of governance frameworks in mitigating risks and building trust in AI implementations within healthcare organizations. As AI technologies rapidly evolve, robust governance is essential to manage potential adverse incidents and ensure fair, equitable, and effective innovation. This article highlights key risks associated with AI deployments and proposes enhancements