Machine learning in medicine: Addressing ethical challenges

PLoS Medicine
(Accessed 10 Nov 2018 )

Machine learning in medicine: Addressing ethical challenges
Effy Vayena, Alessandro Blasimme, I. Glenn Cohen
| published 06 Nov 2018 PLOS Medicine
The clinical use of MLm may transform existing modes of healthcare delivery. MLm will be used in the clinical setting by healthcare professionals, be embedded in smart devices through the internet of things, and be used by patients themselves beyond the clinical setting for disease self-management of chronic conditions. The exponential growth of investment in MLm signals that research is accelerating, and more products may soon be targeting market entry. To merit the trust of patients and adoption by providers, MLm must fully align with data protection requirements, minimize the effects of bias, be effectively regulated, and achieve transparency. Addressing such ethical and regulatory issues as soon as possible is essential for avoiding unnecessary risks and pitfalls that will hinder further progress of MLm.