With over a decade of dedicated involvement in pioneering healthcare innovation, Onicio Leal Neto is resolutely committed to accelerating the digital transformation within the realm of data-driven health and epidemiology. His enduring passion has culminated in the establishment of Epitrack, a forward-thinking venture that serves as a bridge between scientific knowledge and the practical health needs of society. Through Epitrack, he aspires to facilitate the seamless integration of cutting-edge innovations into the fabric of everyday life, ensuring they are not only impactful but also easily accessible and relevant.In addition to his entrepreneurial endeavors, Onicio holds a pivotal role as a researcher associated with the University of Zürich. This unique combination of roles positions him as a dynamic force, effectively uniting academic rigor with real-world innovation to tackle pressing health challenges head-on. His comprehensive approach allows him to holistically address issues, making meaningful contributions that have a lasting influence on healthcare practices and systems.Onicio Leal Neto's remarkable journey encompasses both the visionary realm of entrepreneurship and the steadfast pursuit of scientific breakthroughs, all with the overarching goal of transforming the healthcare landscape for the better.
Ethics, Ethics and AI, Linear vs. Exponential, Regulation and Policy
We live in a data-driven world that needs to leverage organizations, results and impact with data. We also experience an excess of techniques and tools to reach this goal. More specifically, data visualization tools need to be more understood to avoid creating more difficulties in decision-making due to exaggeration and overload of information presented. In contrast, the trend of data stories has been the new way of building narratives with contextualized data, shortening the path to better decisions. In addition, in applying Machine Learning for data analysis and decision-making, one cannot ignore the Fairness and Bias aspects that these machines may have to avoid automating inequalities. In this session, which can have a lecture or workshop format, we address theneed to reframe how hypotheses and data products are built to reduce understanding asymmetries within organizations. Methodologies such as Data Product Canvas, Data Stories and Experiment Design are current tools that need to be part of the routine of analytical teams. So that fragile foundations do not compromise the leverage of data. Aspects of Explainable AIs (XAIs) are also addressed to tackle ethical challenges in implementing essential data analysis routines. The expected results of this session are the construction of a homogeneous level of understanding about these aspects of application methods, criticism, ethics and optimization of data routines.
Future Forecasting, Uncertainty, Change Management, Lifelong Learning
What if a time machine allowed us to travel back 30 years to share this outlook with the health industry at that time, we bet that no one would have believed it. Understanding how these technologies became feasible and widely accepted helps make sense of the past and anticipate what might be coming in the future. However, what is the use of advances in these technologies if they need to be better distributed globally, especially for those who need them most? In this section, we will explore the following topics: (1) main unexpected applications of new data streams to population health; (2) understanding the importance of re-purposing technologies for the democratization of access to health; (3) point out the ethical and fairness challenges that artificial intelligence must consider in order not to automate inequalities; and (4) bring fresh perspectives on the global health challenges that lie ahead and what technologies will meet them.