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  • Perspective
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Direct-to-consumer medical machine learning and artificial intelligence applications

Nature Machine Intelligence volume 3, pages 283–287 (2021)Cite this article

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Abstract

Direct-to-consumer medical artificial intelligence/machine learning applications are increasingly used for a variety of diagnostic assessments, and the emphasis on telemedicine and home healthcare during the COVID-19 pandemic may further stimulate their adoption. In this Perspective, we argue that the artificial intelligence/machine learning regulatory landscape should operate differently when a system is designed for clinicians/doctors as opposed to when it is designed for personal use. Direct-to-consumer applications raise unique concerns due to the nature of consumer users, who tend to be limited in their statistical and medical literacy and risk averse about their health outcomes. This creates an environment where false alarms can proliferate and burden public healthcare systems and medical insurers. While similar situations exist elsewhere in medicine, the ease and frequency with which artificial intelligence/machine learning apps can be used, and their increasing prevalence in the consumer market, calls for careful reflection on how to effectively regulate them. We suggest regulators should strive to better understand how consumers interact with direct-to-consumer medical artificial intelligence/machine learning apps, particularly diagnostic ones, and this requires more than a focus on the system’s technical specifications. We further argue that the best regulatory review would also consider such technologies’ social costs under widespread use.

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Acknowledgements

S.G. and I.G.C. were supported by a grant from the Collaborative Research Program for Biomedical Innovation Law, a scientifically independent collaborative research program supported by a Novo Nordisk Foundation grant (NNF17SA0027784).

Author information

Authors and Affiliations

  1. INSEAD, Fontainebleau, France

    Boris Babic & Theodoros Evgeniou

  2. INSEAD, Singapore, Singapore

    Boris Babic & Theodoros Evgeniou

  3. The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School, The Project on Precision Medicine, Artificial Intelligence, and the Law (PMAIL), Cambridge, MA, USA

    Sara Gerke

  4. Harvard Law School, Cambridge, MA, USA

    I. Glenn Cohen

Authors
  1. Boris Babic
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  2. Sara Gerke
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  3. Theodoros Evgeniou
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  4. I. Glenn Cohen
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Contributions

All authors contributed equally to the analysis and drafting of the paper.

Corresponding author

Correspondence to I. Glenn Cohen.

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Competing interests

I.G.C. served as a bioethics consultant for Otsuka on their Abilify MyCite product. I.G.C. is a member of the Illumina Ethics Advisory Board. The other authors declare no competing interests.

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Peer review information Nature Machine Intelligence thanks Jan Brauner, Geoff Tison and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Babic, B., Gerke, S., Evgeniou, T. et al. Direct-to-consumer medical machine learning and artificial intelligence applications. Nat Mach Intell 3, 283–287 (2021). https://doi.org/10.1038/s42256-021-00331-0

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