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Machine Learning for Health: Algorithm Auditing & Quality Control.

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 scree...

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Published in:Journal of Medical Systems Vol. 45; no. 12; pp. 1 - 9
Main Authors: Oala, Luis, Murchison, Andrew G., Balachandran, Pradeep, Choudhary, Shruti, Fehr, Jana, Leite, Alixandro Werneck, Goldschmidt, Peter G., Johner, Christian, Schörverth, Elora D. M., Nakasi, Rose, Meyer, Martin, Cabitza, Federico, Baird, Pat, Prabhu, Carolin, Weicken, Eva, Liu, Xiaoxuan, Wenzel, Markus, Vogler, Steffen, Akogo, Darlington, Alsalamah, Shada
Format: editorial tables/charts Journal Article
Published: Springer Nature Dec2021
Online Access:View this record in EBSCOhost
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      dt: Dec2021
      vid: 45
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      pub: Springer Nature
      place: New York, New York
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        atl: Machine Learning for Health: Algorithm Auditing & Quality Control.
      aug:
        au:
          Oala, Luis
          Murchison, Andrew G.
          Balachandran, Pradeep
          Choudhary, Shruti
          Fehr, Jana
          Leite, Alixandro Werneck
          Goldschmidt, Peter G.
          Johner, Christian
          Schörverth, Elora D. M.
          Nakasi, Rose
          Meyer, Martin
          Cabitza, Federico
          Baird, Pat
          Prabhu, Carolin
          Weicken, Eva
          Liu, Xiaoxuan
          Wenzel, Markus
          Vogler, Steffen
          Akogo, Darlington
          Alsalamah, Shada
        affil: Fraunhofer HHI, Berlin, Germany
      sug:
        subj:
          Health Care Industry
          Machine Learning Utilization
          Algorithms
          Quality Control (Technology)
          Audit
          Artificial Intelligence
          Software
          Decision Support Systems, Clinical
          Checklists
      ab: Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.
      pubtype: Academic Journal
      doctype:
        editorial
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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