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Big Data In Medicine: Challenges and Opportunities
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Europe/Zurich
503/1-001 - Council Chamber (CERN)
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Description
CERN and the high-energy physics (HEP) community have pioneered the use of large-scale, distributed, data-driven research models. In recent years, other research communities have started collecting and processing increasingly large amounts of data and are now facing very similar challenges in terms of infrastructures, technologies and software applications. At the same time, the increasing adoption of sophisticated artificial-intelligence (AI) methods and high-performance hardware platforms offers great potential benefits, but these are accompanied by concerns about the impact of these technologies on society.
In 2017, CERN adopted a specific knowledge-transfer strategy for medical applications. This strategy focuses on sharing knowledge and ideas with the medical and healthcare communities, as well as fostering cross-domain collaboration. It rests on three main areas: particle-accelerator technologies, sensors and imaging, and large-scale computing.
This workshop marks the conclusion of the first two years of pilot investigations on the impact of computing and data-science research at CERN, and opens the way to broader collaboration with the medical and healthcare research communities. The main aims of the workshop are to examine the major societal challenges in healthcare; identify the infrastructural requirements needed to enable large-scale, distributed collaborative research; and assess the potential benefits of emerging AI-driven applications across a broad range of research and clinical domains. Through the workshop, we also aim to identify the possible ethical and technical shortcomings of such approach.
The input collected during the workshop will be used to develop a white paper highlighting the challenges faced and outlining a roadmap for maximising the impact of CERN’s work in this area. This will help CERN to structure its collaboration and set appropriate objectives for the future.
