期刊
BIOESSAYS
卷 43, 期 10, 页码 -出版社
WILEY
DOI: 10.1002/bies.202100025
关键词
artificial intelligence; causality; genomic medicine; molecular genetics; policy implications; scientific understanding
资金
- Bundesministerium fur Bildung und Forschung, BMBF [16ITA201A]
With the increasing use of artificial intelligence in genomic research and medicine, integrating AI methods with traditional causal concepts will have a significant impact on future scientific understanding and self-conceptions, and will be crucial in developing differentiated policies.
The increasing availability of large-scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that-while capable of generating predictive knowledge at unprecedented pace and scale-if and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self-conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine.
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