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Rejoinder to Discussions on “Approval policies for modifications to machine learning‐based software as a medical device: A study of bio‐creep”

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  • Jean Feng
  • Scott Emerson
  • Noah Simon

Abstract

We thank the discussants for sharing their unique perspectives on the problem of designing automatic algorithm change protocols (aACPs) for machine learning‐based software as a medical device. Both Pennello et al. and Rose highlighted a number of challenges that arise in real‐world settings, and we whole‐heartedly agree that substantial extensions of our work are needed to understand if and how aACPs can be safely deployed in practice. Our work demonstrated that aACPs that appear to be harmless may allow for biocreep, even when the data distribution is assumed to be representative and stationary over time. While we investigated two solutions that protect against this specific issue, many more statistical and practical challenges remain and we look forward to future research on this topic.

Suggested Citation

  • Jean Feng & Scott Emerson & Noah Simon, 2021. "Rejoinder to Discussions on “Approval policies for modifications to machine learning‐based software as a medical device: A study of bio‐creep”," Biometrics, The International Biometric Society, vol. 77(1), pages 52-53, March.
  • Handle: RePEc:bla:biomet:v:77:y:2021:i:1:p:52-53
    DOI: 10.1111/biom.13380
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    Cited by:

    1. Sherri Rose, 2021. "Discussion on “Approval policies for modifications to machine learning‐based software as a medical device: A study of biocreep” by Jean Feng, Scott Emerson, and Noah Simon," Biometrics, The International Biometric Society, vol. 77(1), pages 49-51, March.

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