Machine learning and genomics: precision medicine versus patient privacy
By Chloé-Agathe Azencott. 2018
68 citations recorded by the source corpus.
Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues. We review how breaches in patient privacy can occur, present recent developments in computational data protection and discuss how they can be combined with legal and ethical perspectives to provide secure frameworks for genomic data sharing. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.
Subfields: Artificial Intelligence, Health Informatics, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Or Sheffet
- Aleksandra Korolova
- Uri Stemmer
- Kobbi Nissim
- Amos Beimel
- Jonathan Ullman
- Vitaly Feldman
- Mark Bun
In Information Systems
Citations in the corpus
- The power of synergy in differential privacy: Combining a small curator with local randomizers (2019, background): “Solving the problem while ensuring privacy is particularly challenging when the feature dimension is large compared to the number of available samples, which is the case for GWAS [13, 3, 39, 57].”
- Artificial Intelligence Transforms the Future of Health Care. (2019, background): “59 At the same time, healthcare institutions need to communicate with the public and collaborate with scientific communities, as well as government agencies.”