Artificial Intelligence is transforming healthcare, promising great advances in disease detection and treatment. Our understanding of the impact these technologies may have on people, systems and societies is limited, however, especially when it comes to applications in low and middle-income countries. How do we guarantee that AI-assisted technologies don’t perpetuate bias? How do we validate their findings, and assess if they actually improve health outcomes? How do we ensure these technologies are designed by and for local communities? And how do we encourage the development of new sources of data to train machine learning algorithms, especially in data-poor environments?
On February 26th, the Harvard Global Health Institute hosted a major summit taking a critical, cross-disciplinary look at the future of AI in global health. By bringing together leading scientists, researchers, policy makers, and practitioners separated hype from reality and mapped out an agenda for collaboration and research that will lead toward better-informed decision-making on privacy, safety, equity, scalability, health system integration, business models and other pressing questions emerging with the rise of AI in healthcare.
For continued updates, browse the blog and follow #GlobalHealthAI