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Co‐designing diagnosis: Towards a responsible integration of Machine Learning decision‐support systems in medical diagnostics

RATIONALE: This paper aims to show how the focus on eradicating bias from Machine Learning decision‐support systems in medical diagnosis diverts attention from the hermeneutic nature of medical decision‐making and the productive role of bias. We want to show how an introduction of Machine Learning s...

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Autores principales: Kudina, Olya, de Boer, Bas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248235/
https://www.ncbi.nlm.nih.gov/pubmed/33480150
http://dx.doi.org/10.1111/jep.13535
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author Kudina, Olya
de Boer, Bas
author_facet Kudina, Olya
de Boer, Bas
author_sort Kudina, Olya
collection PubMed
description RATIONALE: This paper aims to show how the focus on eradicating bias from Machine Learning decision‐support systems in medical diagnosis diverts attention from the hermeneutic nature of medical decision‐making and the productive role of bias. We want to show how an introduction of Machine Learning systems alters the diagnostic process. Reviewing the negative conception of bias and incorporating the mediating role of Machine Learning systems in the medical diagnosis are essential for an encompassing, critical and informed medical decision‐making. METHODS: This paper presents a philosophical analysis, employing the conceptual frameworks of hermeneutics and technological mediation, while drawing on the case of Machine Learning algorithms assisting doctors in diagnosis. This paper unravels the non‐neutral role of algorithms in the doctor's decision‐making and points to the dialogical nature of interaction not only with the patients but also with the technologies that co‐shape the diagnosis. FINDINGS: Following the hermeneutical model of medical diagnosis, we review the notion of bias to show how it is an inalienable and productive part of diagnosis. We show how Machine Learning biases join the human ones to actively shape the diagnostic process, simultaneously expanding and narrowing medical attention, highlighting certain aspects, while disclosing others, thus mediating medical perceptions and actions. Based on that, we demonstrate how doctors can take Machine Learning systems on board for an enhanced medical diagnosis, while being aware of their non‐neutral role. CONCLUSIONS: We show that Machine Learning systems join doctors and patients in co‐designing a triad of medical diagnosis. We highlight that it is imperative to examine the hermeneutic role of the Machine Learning systems. Additionally, we suggest including not only the patient, but also colleagues to ensure an encompassing diagnostic process, to respect its inherently hermeneutic nature and to work productively with the existing human and machine biases.
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spelling pubmed-82482352021-07-06 Co‐designing diagnosis: Towards a responsible integration of Machine Learning decision‐support systems in medical diagnostics Kudina, Olya de Boer, Bas J Eval Clin Pract Original Paper RATIONALE: This paper aims to show how the focus on eradicating bias from Machine Learning decision‐support systems in medical diagnosis diverts attention from the hermeneutic nature of medical decision‐making and the productive role of bias. We want to show how an introduction of Machine Learning systems alters the diagnostic process. Reviewing the negative conception of bias and incorporating the mediating role of Machine Learning systems in the medical diagnosis are essential for an encompassing, critical and informed medical decision‐making. METHODS: This paper presents a philosophical analysis, employing the conceptual frameworks of hermeneutics and technological mediation, while drawing on the case of Machine Learning algorithms assisting doctors in diagnosis. This paper unravels the non‐neutral role of algorithms in the doctor's decision‐making and points to the dialogical nature of interaction not only with the patients but also with the technologies that co‐shape the diagnosis. FINDINGS: Following the hermeneutical model of medical diagnosis, we review the notion of bias to show how it is an inalienable and productive part of diagnosis. We show how Machine Learning biases join the human ones to actively shape the diagnostic process, simultaneously expanding and narrowing medical attention, highlighting certain aspects, while disclosing others, thus mediating medical perceptions and actions. Based on that, we demonstrate how doctors can take Machine Learning systems on board for an enhanced medical diagnosis, while being aware of their non‐neutral role. CONCLUSIONS: We show that Machine Learning systems join doctors and patients in co‐designing a triad of medical diagnosis. We highlight that it is imperative to examine the hermeneutic role of the Machine Learning systems. Additionally, we suggest including not only the patient, but also colleagues to ensure an encompassing diagnostic process, to respect its inherently hermeneutic nature and to work productively with the existing human and machine biases. John Wiley & Sons, Inc. 2021-01-22 2021-06 /pmc/articles/PMC8248235/ /pubmed/33480150 http://dx.doi.org/10.1111/jep.13535 Text en © 2021 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Original Paper
Kudina, Olya
de Boer, Bas
Co‐designing diagnosis: Towards a responsible integration of Machine Learning decision‐support systems in medical diagnostics
title Co‐designing diagnosis: Towards a responsible integration of Machine Learning decision‐support systems in medical diagnostics
title_full Co‐designing diagnosis: Towards a responsible integration of Machine Learning decision‐support systems in medical diagnostics
title_fullStr Co‐designing diagnosis: Towards a responsible integration of Machine Learning decision‐support systems in medical diagnostics
title_full_unstemmed Co‐designing diagnosis: Towards a responsible integration of Machine Learning decision‐support systems in medical diagnostics
title_short Co‐designing diagnosis: Towards a responsible integration of Machine Learning decision‐support systems in medical diagnostics
title_sort co‐designing diagnosis: towards a responsible integration of machine learning decision‐support systems in medical diagnostics
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248235/
https://www.ncbi.nlm.nih.gov/pubmed/33480150
http://dx.doi.org/10.1111/jep.13535
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