Ethical layering in AI-driven polygenic risk scores—New complexities, new challenges

Marie Christine Fritzsche, Kaya Akyüz, Mónica Cano Abadía, Stuart McLennan, Pekka Marttinen, Michaela Th Mayrhofer, Alena M. Buyx

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish
Article number1098439
JournalFrontiers in Genetics
StatePublished - 26 Jan 2023


  • artificial intelligence–AI
  • deep neural network (DNN)
  • ethical
  • genomics
  • machine learning (ML)
  • polygenic risk score
  • predictive medicine
  • stratification


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