New publication: Machine learning in medicine: Ethical challenges and how to move forward by Effy Vayena, Alessandro Blasimme, I. Glenn Cohen

The authors illustrate in “external pageMachine learning in medicine: Ethical challenges and how to move forwardcall_made,” the many concerns about MLm algorithms entry into practice, including data sourcing and data protection, transparency and accountability, and the development and deployment of predictive algorithms, which could exacerbate biases and undermine patient autonomy, among other ethical difficulties.

Guidance from regulators, with input from medical practitioners and developers, is key to addressing these issues, and the paper identifies a number of ethical and legal questions that the authors suggest regulatory guidance could provide leadership on, including:

• The disclosure of basic, yet meaningful, details about MLm-based patient assessments
  and treatment suggestions.
• The grounds for liability for adverse events related to the use of MLm.
• Consent for use of patient data in accordance with the local regulations of where the
  data originated.
• Best practices for minimizing the introduction of bias and mitigating downstream
  effects.

“Standards—both ethical and technical — are key to fulfilling these aims,” caution the authors. “Regulators must develop standard procedures, including effective post-marketing monitoring mechanisms.”

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Acknowledgement: I. Glenn Cohen’s research was supported by a Novo Nordisk Foundation-grant for a Collaborative Research Programme (grant agreement number NNF17SA027784).
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Citation: Vayena E, Blasimme A, Cohen IG (2018) Machine learning in medicine: Addressing ethical challenges. PLoS Med 15(11): e1002689. external pagehttps://doi.org/10.1371/journal.pmed.1002689call_made