Machine Learning for Health: Algorithm Auditing & Quality Control

Luis Oala; Andrew G. Murchison; Pradeep Balachandran; Shruti Choudhary; Jana Fehr; Alixandro Werneck Leite; Peter G. Goldschmidt; Christian Johner; Elora D.M. Schörverth; Rose Nakasi; Martin Meyer; Federico Cabitza; Pat Baird; Carolin Prabhu; Eva Weicken; Xiaoxuan Liu; Markus Wenzel; Steffen Vogler; Darlington Akogo; Shada Alsalamah; Emre Kazim; Adriano Koshiyama; Sven Piechottka; Sheena Macpherson; Ian Shadforth; Regina Geierhofer; Christian Matek; Joachim Krois; Bruno Sanguinetti; Matthew Arentz; Pavol Bielik; Saul Calderon-Ramirez; Auss Abbood; Nicolas Langer; Stefan Haufe; Ferath Kherif; Sameer Pujari; Wojciech Samek; Thomas Wiegand

doi:10.1007/s10916-021-01783-y

Machine Learning for Health: Algorithm Auditing & Quality Control

Luis Oala
, Andrew G. Murchison
, Pradeep Balachandran
, Shruti Choudhary
, Jana Fehr
, Alixandro Werneck Leite
, Peter G. Goldschmidt
, Christian Johner
, Elora D.M. Schörverth
, Rose Nakasi
, Martin Meyer
, Federico Cabitza
, Pat Baird
, Carolin Prabhu
, Eva Weicken
, Xiaoxuan Liu
, Markus Wenzel
, Steffen Vogler
, Darlington Akogo
, Shada Alsalamah

Emre Kazim, Adriano Koshiyama, Sven Piechottka, Sheena Macpherson, Ian Shadforth, Regina Geierhofer, Christian Matek, Joachim Krois, Bruno Sanguinetti, Matthew Arentz, Pavol Bielik, Saul Calderon-Ramirez, Auss Abbood, Nicolas Langer, Stefan Haufe, Ferath Kherif, Sameer Pujari, Wojciech Samek, Thomas Wiegand

Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute
Oxford University Hospitals NHS Foundation Trust
Technical Consultant (Digital Health)
University of Oxford
University of Potsdam
Universidade de Brasília
World Development Group Inc
Johner Institute
Makerere University
Siemens
University of Milan - Bicocca
Philips United States
Office of the Auditor General of Norway
University Hospitals Birmingham NHS Foundation Trust
Bayer AG
minoHealth AI Labs
King Saud University
World Health Organization
University College London
Open Regulatory
MIOTIFY LTD
Helmholtz Zentrum München - German Research Center for Environmental Health
Charité – Universitätsmedizin Berlin
Dotphoton AG
University of Washington
ETH Zürich
Robert Koch-Institut
University of Zurich
Technical University of Berlin
University of Lausanne

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46 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Número de artículo	105
Publicación	Journal of Medical Systems
Volumen	45
N.º	12
DOI	https://doi.org/10.1007/s10916-021-01783-y
Estado	Publicada - dic 2021

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Oala, L., Murchison, A. G., Balachandran, P., Choudhary, S., Fehr, J., Leite, A. W., Goldschmidt, P. G., Johner, C., Schörverth, E. D. M., Nakasi, R., Meyer, M., Cabitza, F., Baird, P., Prabhu, C., Weicken, E., Liu, X., Wenzel, M., Vogler, S., Akogo, D., ... Wiegand, T. (2021). Machine Learning for Health: Algorithm Auditing & Quality Control. Journal of Medical Systems, 45(12), Artículo 105. https://doi.org/10.1007/s10916-021-01783-y

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title = "Machine Learning for Health: Algorithm Auditing \& Quality Control",

abstract = "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.",

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month = dec,

doi = "10.1007/s10916-021-01783-y",

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Oala, L, Murchison, AG, Balachandran, P, Choudhary, S, Fehr, J, Leite, AW, Goldschmidt, PG, Johner, C, Schörverth, EDM, Nakasi, R, Meyer, M, Cabitza, F, Baird, P, Prabhu, C, Weicken, E, Liu, X, Wenzel, M, Vogler, S, Akogo, D, Alsalamah, S, Kazim, E, Koshiyama, A, Piechottka, S, Macpherson, S, Shadforth, I, Geierhofer, R, Matek, C, Krois, J, Sanguinetti, B, Arentz, M, Bielik, P, Calderon-Ramirez, S, Abbood, A, Langer, N, Haufe, S, Kherif, F, Pujari, S, Samek, W & Wiegand, T 2021, 'Machine Learning for Health: Algorithm Auditing & Quality Control', Journal of Medical Systems, vol. 45, n.º 12, 105. https://doi.org/10.1007/s10916-021-01783-y

TY - JOUR

T1 - Machine Learning for Health

T2 - Algorithm Auditing & Quality Control

AU - Oala, Luis

AU - Murchison, Andrew G.

AU - Balachandran, Pradeep

AU - Choudhary, Shruti

AU - Fehr, Jana

AU - Leite, Alixandro Werneck

AU - Goldschmidt, Peter G.

AU - Johner, Christian

AU - Schörverth, Elora D.M.

AU - Nakasi, Rose

AU - Meyer, Martin

AU - Cabitza, Federico

AU - Baird, Pat

AU - Prabhu, Carolin

AU - Weicken, Eva

AU - Liu, Xiaoxuan

AU - Wenzel, Markus

AU - Vogler, Steffen

AU - Akogo, Darlington

AU - Alsalamah, Shada

AU - Kazim, Emre

AU - Koshiyama, Adriano

AU - Piechottka, Sven

AU - Macpherson, Sheena

AU - Shadforth, Ian

AU - Geierhofer, Regina

AU - Matek, Christian

AU - Krois, Joachim

AU - Sanguinetti, Bruno

AU - Arentz, Matthew

AU - Bielik, Pavol

AU - Calderon-Ramirez, Saul

AU - Abbood, Auss

AU - Langer, Nicolas

AU - Haufe, Stefan

AU - Kherif, Ferath

AU - Pujari, Sameer

AU - Samek, Wojciech

AU - Wiegand, Thomas

PY - 2021/12

Y1 - 2021/12

N2 - 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.

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.

KW - Algorithm

KW - Artificial intelligence

KW - Auditing

KW - Health

KW - Machine learning

KW - Quality control

UR - https://www.scopus.com/pages/publications/85118701534

U2 - 10.1007/s10916-021-01783-y

DO - 10.1007/s10916-021-01783-y

M3 - Artículo

C2 - 34729675

AN - SCOPUS:85118701534

SN - 0148-5598

VL - 45

JO - Journal of Medical Systems

JF - Journal of Medical Systems

IS - 12

M1 - 105

ER -

Machine Learning for Health: Algorithm Auditing & Quality Control

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