AI models and the future of genomic research and medicine: True sons of knowledge?: Artificial intelligence needs to be integrated with causal conceptions in biomedicine to harness its societal benefits for the field. (11th August 2021)

• Record Type: Journal Article
• Authors: König, Harald; Frank, Daniel; Baumann, Martina; Heil, Reinhard
• Is Part Of: BioEssays. Volume 43:Number 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-11
• Subjects: artificial intelligence -- causality -- genomic medicine -- molecular genetics -- policy implications -- scientific understanding; Molecular biology -- Periodicals; Cytology -- Periodicals; Developmental biology -- Periodicals; 572.8
• DOI: 10.1002/bies.202100025
• Languages: English
• ISSNs: 0265-9247

Abstract

Abstract: 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. Abstract : Associative and black‐box AI models contrast with intervention‐based causal concepts in molecular genetics and biomedicine. Policies should consider that harnessing societal benefits requires to integrate these models with needs for and accounts of causal knowledge. Such integration will depend on technological progress as well as scientific paradigms and stakeholder perceptions.