Titel
Ethical layering in AI-driven polygenic risk scores—New complexities, new challenges
Autor*in
Marie-Christine Fritzsche
Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich
Autor*in
Mónica Cano Abadía
Biobanking and Biomolecular Resources Research Infrastructure Consortium - European Research Infrastructure Consortium (BBMRI-ERIC)
... show all
Abstract
Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.
Stichwort
genomicspolygenic risk scoredeep neural network (DNN)machine learning (ML)artificial intelligence–AIstratificationpredictive medicineethical
Objekt-Typ
Sprache
Englisch [eng]
Erschienen in
Titel
Frontiers in Genetics
Band
14
ISSN
1664-8021
Erscheinungsdatum
2023
Publication
Frontiers Media SA
Erscheinungsdatum
2023
Zugänglichkeit
Rechteangabe
© 2023 Fritzsche, Akyüz, Cano Abadía, McLennan, Marttinen, Mayrhofer and Buyx

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