Title : Data-Driven or Data-Denial? Foundations for responsible decision-making in the age of AI and longevity science
Abstract:
In an era where aging research, digital health, and AI-enabled care generate more data in a single minute than entire decades once produced, the challenge is no longer data scarcity; it's data usability. As the fields of geroscience, longevity medicine, and AI-supported clinical care rapidly evolve, the ability to make sound, data?driven decisions becomes the cornerstone of meaningful progress. Yet a persistent gap remains: while organizations across healthcare and aging science acknowledge the importance of data-informed strategy, far fewer successfully operationalize it. This keynote explores the critical foundations of data-driven decision-making that empower breakthroughs across aging and longevity. Building on Albert Einstein’s timeless reminder that “not everything that counts can be counted,” we examine how to distinguish meaningful signals from overwhelming noise; an essential skill when navigating imaging datasets, omics pipelines, wearables, sensor-derived digital biomarkers, and AI-powered risk prediction tools for older adults. Attendees will learn how cognitive biases derail evidence-based choices, how qualitative insights complement quantitative models, and how the “Five Phases” framework (LOOK, LINK, LISTEN, LEVERAGE, and LEARN) can transform raw data into trustworthy and actionable guidance. Whether applied to geriatric care delivery, predictive modelling, or policy design for aging societies, these principles provide a high-level yet practical foundation for ensuring that the accelerating data landscape becomes a delight, not a disaster.
