Jacques S. Beckmann, University of Lausanne, Switzerland
The recent years have seen the emergence of both ground-breaking scientific developments in high-resolution, high-throughput data gathering technologies enabling cost-effective collection and analysis of huge, disparate datasets on individual health, as well as of sophisticated clinical bioinformatics or machine learning tools / artificial intelligence required for the analyses and interpretation of this wealth of data. These developments triggered numerous initiatives in precision medicine (PM), a data-driven and currently still, essentially a highly genome-centric initiative (additional dimensions being progressively integrated).
Proper and effective delivery of PM poses numerous challenges. Foremost, PM needs to be contrasted with the powerful practice of evidence-based medicine (EBM), essentially informed by meta-analyses or group-centered studies from which mean recommendations are derived. These amount at first approximation to a “one size fits all” approach, which does not provide adequate solutions for outliers. Yet, we are all outliers for one or another trait. In contrast to EBM, one of the strengths of PM, which allows for a systemic, holistic approach focusing on the individual, including outliers, lies in the area of personalized management leading eventually to improved and sustained well-being.
We will discuss challenges and opportunities towards these goals.