Global Perspectives on Digital Health

Episode 2: Practical Solutions to Health Data PovertyIn this episode, we continue our exploration of health data poverty, showcasing a team that's making a real difference based on the issues discussed in Part 1 with Dr. Xiao Liu.We're thrilled to have spoken with Prof. Alexandre Filho, a Professor of Machine Learning in Sao Paulo, Brazil. He shared insights on how his team has been maximizing the impact of data-driven technology in Brazil. Prof. Filho's remark, "The world is becoming more like Brazil," sets the stage for a discussion on what the global community can learn from Brazil's approach to data diversity, especially in serving underserved communities.Key Topics:Global Lessons from Local Successes: How Brazil's high-quality, diverse data sets serve as a model for the world.Overcoming Data Challenges: Tackling issues with data availability and the performance limitations of algorithms developed in affluent settings when deployed in rural areas.Leveraging Local Data: Details on the team's work, including a paper on neonatal mortality prediction using routinely collected data.Direct Benefits to Data Providers: Ensuring that those from whom data is collected see benefits from its use.Balancing Impact and Scalability: The trade-offs between highly tuned, local solutions and the need for scalable models that perform well across broader contexts.Benchmarking for Better Health: Extended efforts in the ITU/WHO focus group on AI for health, evaluating the performance of models across different LMIC settings.Advancing Local Impact: The use of transfer learning to enhance model performance and impact locally.Prof. Filho’s Recommendations: Key advice for innovators and implementers in the EU, US, and UK.Guest Bio:Alexandre Chiavegatto Filho is an Associate Professor of Machine Learning in Healthcare at the Department of Epidemiology, School of Public Health, University of São Paulo. He directs the Laboratory of Big Data and Predictive Analysis in Health (Labdaps), which includes a team of 30 researchers focused on developing AI algorithms to improve healthcare decisions.Find the team's work on Google Scholar 

Episode 1: Health data poverty : what can we do about it?Join us as we dive into a crucial conversation with Dr. Xiao Liu, a leading figure in AI ethics, evaluation, and regulation. This episode focuses on one of the most pressing issues in the field: health data poverty. We cover what it is, and how we might go about addressing this. Key Topics:Defining Health Data Poverty: What it means and why it matters for global health.Relevance to Developed Nations: Understanding the implications for the US, EU, and UK.In-Depth Analysis: Discussing a 2021 Lancet paper co-authored by Dr. Liu.Real-world Examples: Exploring instances of health data poverty and its impacts.Expert Recommendations: Dr. Liu shares her views on current progress and the steps ahead.Broader Implications: How can society and the healthcare ecosystem address these challenges comprehensively?Looking Ahead:Stay tuned for Part 2, where we will showcase implemented initiatives addressing these issues in Brazil.Resources:Standing Together Lancet articlehttps://www.datadiversity.org/

Welcome to the trailer for the Global Perspectives on Digital Health Podcast. Join Shubs as he unpacks insights from experts and innovators creating real impact for underserved communities around the world. Shubs is a physician working in digital health. With experience across product development and deployment, regulation and also with the ITU/WHO focus group AI for healthcare as co-chair of the working group on clinical evaluation Shubs has seen the challenges across the digital and healthcare ecosystem. Join these conversations as he seeks out innovators and experts to unpack what we can do to create real impact for those who need innovation the most around the world. Get a truly global perspective on digital health.