(2020). COVID-19 and social-politics of medical misinformation on social media in Pakistan. Media Asia. Ahead of Print.

This scoping review examined racial and ethnic bias in artificial intelligence health algorithms (AIHA), the role of stakeholders in oversight, and the consequences of AIHA for health equity. Using the PRISMA-ScR guidelines, databases were searched between 2020 and 2024 using the terms racial and ethnic bias in health algorithms resulting in a final sample of 23 sources. Suggestions for how to mitigate algorithmic bias were compiled and evaluated, roles played by stakeholders were identified, and governance and stewardship plans for AIHA were examined. While AIHA represent a significant breakthrough in predictive analytics and treatment optimization, regularly outperforming humans in diagnostic precision and accuracy, they also present serious challenges to patient privacy, data security, institutional transparency, and health equity. Evidence from extant sources including those in this review showed that AIHA carry the potential to perpetuate health inequities. While the current study considered AIHA in the US, the use of AIHA carries implications for global health equity.

Background: Artificial intelligence (AI) health applications developed in Global North countries are being diffused to the Global South but not without problems. Objective: The goal of this integrative scoping review was to identify recent studies from 2022 to 2025 describing the contributions and challenges in using AI health applications in the Global South. Methods: An integrative scoping review of 24 studies from seven databases identified four themes. Major Findings: This review adds to previous investigations by 1) focusing on data interoperability to resolve gaps in digitizing global health data required for accurate machine learning, 2) examining formidable economic and infrastructure hurdles for adoption and implementation of health AI in low-resource countries, 3) identifying barriers faced by emerging economies for partnerships with global AI biotech companies in AI health startups, and 4) calling for regulations and global surveillance of AI health applications. Conclusions: Practical implications for the future use of AI health applications in the Global South are discussed concluding that applications developed and trained on datasets from high income countries need to be recalibrated to work in the Global South. Additionally, due to the health-related disparities within the Global North the adoption of AI needs to be considered in its own context.

Today, college students are dealing with depression at some of the highest rates in decades. As the primary mental health service provider, university counseling centers are limited in their capacity and efficiency to provide mental health care due to time constraints and reliance on students' self-reports. A mobile behavioral-sensing platform may serve as a solution to enhance the efficiency and accessibility of university counseling services. The main objectives of this study are to (1) understand the usefulness of a mobile sensing platform (ie, iSee) in improving counseling services and assisting students' self-management of their depression conditions, and (2) explore what types of behavioral targets (ie, meaningful information extracted from raw sensor data) and feedback to deliver from both clinician and students' perspectives. We conducted semistructured interviews with 9 clinicians and 12 students with depression recruited from a counseling center at a large Midwestern university. The interviews were 40-50 minutes long and were audio recorded and transcribed. The interview data were analyzed using thematic analysis with an inductive approach. Clinician and student interviews were analyzed separately for comparison. The process of extracting themes involved iterative coding, memo writing, theme revisits, and refinement. From the clinician perspective, the mobile sensing platform helps to improve counseling service by providing objective evidence for clinicians and filling gaps in clinician-patient communication. Clinicians suggested providing students with their sensed behavioral targets organized around personalized goals. Clinicians also recommended delivering therapeutic feedback to students based on their sensed behavioral targets, including positive reinforcement, reflection reminders, and challenging negative thoughts. From the student perspective, the mobile sensing platform helps to ease continued self-tracking practices. Students expressed their need for integrated behavioral targets to understand correlations between behaviors and depression. They also pointed out that they would prefer to avoid seeing negative feedback. Although clinician and student participants shared views on the advantages of iSee in supporting university counseling, they had divergent opinions on the types of behavioral targets and feedback to be provided via iSee. This exploratory work gained initial insights into the design of a mobile sensing platform for depression management and informed a more conclusive research project for the future.