Artificial intelligence in human genomics and biomedicine
Dynamics, potentials and challenges
Keywords:artificial intelligence, genomics, biomedicine, knowledge, governance
The increasing availability of extensive and complex data has made human genomics and its applications in (bio)medicine an at tractive domain for artificial intelligence (AI) in the form of advanced machine learning (ML) methods. These methods are linked not only to the hope of improving diagnosis and drug development. Rather, they may also advance key issues in biomedicine, e. g. understanding how individual differences in the human genome may cause specific traits or diseases. We analyze the increasing convergence of AI and genomics, the emergence of a corresponding innovation system, and how these associative AI methods relate to the need for causal knowledge in biomedical research and development (R&D) and in medical practice. Finally, we look at the opportunities and challenges for clinical practice and the implications for governance issues arising from this convergence.
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Copyright (c) 2021 Reinhard Heil, Nils B. Heyen, Martina Baumann, Bärbel Hüsing, Daniel Bachlechner, Ulrich Schmoch, Harald König
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles in TATuP - Journal for Technology Assessment in Theory and Practice are published under the Creative Commons Licence CC BY 4.0.
Bundesministerium für Bildung und Forschung
Grant numbers 16ITA201A/B