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Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned?
We argue why interpretability should have primacy alongside empiricism for several reasons: first, if machine learning (ML) models are beginning to render some of the high-risk healthcare decisions instead of clinicians, these models pose a novel medicolegal and ethical frontier that is incompletely...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9411871/ https://www.ncbi.nlm.nih.gov/pubmed/34006600 http://dx.doi.org/10.1136/medethics-2020-107102 |
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author | Yoon, Chang Ho Torrance, Robert Scheinerman, Naomi |
author_facet | Yoon, Chang Ho Torrance, Robert Scheinerman, Naomi |
author_sort | Yoon, Chang Ho |
collection | PubMed |
description | We argue why interpretability should have primacy alongside empiricism for several reasons: first, if machine learning (ML) models are beginning to render some of the high-risk healthcare decisions instead of clinicians, these models pose a novel medicolegal and ethical frontier that is incompletely addressed by current methods of appraising medical interventions like pharmacological therapies; second, a number of judicial precedents underpinning medical liability and negligence are compromised when ‘autonomous’ ML recommendations are considered to be en par with human instruction in specific contexts; third, explainable algorithms may be more amenable to the ascertainment and minimisation of biases, with repercussions for racial equity as well as scientific reproducibility and generalisability. We conclude with some reasons for the ineludible importance of interpretability, such as the establishment of trust, in overcoming perhaps the most difficult challenge ML will face in a high-stakes environment like healthcare: professional and public acceptance. |
format | Online Article Text |
id | pubmed-9411871 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-94118712022-09-12 Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned? Yoon, Chang Ho Torrance, Robert Scheinerman, Naomi J Med Ethics Clinical Ethics We argue why interpretability should have primacy alongside empiricism for several reasons: first, if machine learning (ML) models are beginning to render some of the high-risk healthcare decisions instead of clinicians, these models pose a novel medicolegal and ethical frontier that is incompletely addressed by current methods of appraising medical interventions like pharmacological therapies; second, a number of judicial precedents underpinning medical liability and negligence are compromised when ‘autonomous’ ML recommendations are considered to be en par with human instruction in specific contexts; third, explainable algorithms may be more amenable to the ascertainment and minimisation of biases, with repercussions for racial equity as well as scientific reproducibility and generalisability. We conclude with some reasons for the ineludible importance of interpretability, such as the establishment of trust, in overcoming perhaps the most difficult challenge ML will face in a high-stakes environment like healthcare: professional and public acceptance. BMJ Publishing Group 2022-09 2021-05-18 /pmc/articles/PMC9411871/ /pubmed/34006600 http://dx.doi.org/10.1136/medethics-2020-107102 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Clinical Ethics Yoon, Chang Ho Torrance, Robert Scheinerman, Naomi Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned? |
title | Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned? |
title_full | Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned? |
title_fullStr | Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned? |
title_full_unstemmed | Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned? |
title_short | Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned? |
title_sort | machine learning in medicine: should the pursuit of enhanced interpretability be abandoned? |
topic | Clinical Ethics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9411871/ https://www.ncbi.nlm.nih.gov/pubmed/34006600 http://dx.doi.org/10.1136/medethics-2020-107102 |
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