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A proposal for developing a platform that evaluates algorithmic equity and accuracy
We are at a pivotal moment in the development of healthcare artificial intelligence (AI), a point at which enthusiasm for machine learning has not caught up with the scientific evidence to support the equity and accuracy of diagnostic and therapeutic algorithms. This proposal examines algorithmic bi...
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/PMC9003600/ https://www.ncbi.nlm.nih.gov/pubmed/35410952 http://dx.doi.org/10.1136/bmjhci-2021-100423 |
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author | Cerrato, Paul Halamka, John Pencina, Michael |
author_facet | Cerrato, Paul Halamka, John Pencina, Michael |
author_sort | Cerrato, Paul |
collection | PubMed |
description | We are at a pivotal moment in the development of healthcare artificial intelligence (AI), a point at which enthusiasm for machine learning has not caught up with the scientific evidence to support the equity and accuracy of diagnostic and therapeutic algorithms. This proposal examines algorithmic biases, including those related to race, gender and socioeconomic status, and accuracy, including the paucity of prospective studies and lack of multisite validation. We then suggest solutions to these problems. We describe the Mayo Clinic, Duke University, Change Healthcare project that is evaluating 35.1 billion healthcare records for bias. And we propose ‘Ingredients’ style labels and an AI evaluation/testing system to help clinicians judge the merits of products and services that include algorithms. Said testing would include input data sources and types, dataset population composition, algorithm validation techniques, bias assessment evaluation and performance metrics. |
format | Online Article Text |
id | pubmed-9003600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-90036002022-04-27 A proposal for developing a platform that evaluates algorithmic equity and accuracy Cerrato, Paul Halamka, John Pencina, Michael BMJ Health Care Inform Review We are at a pivotal moment in the development of healthcare artificial intelligence (AI), a point at which enthusiasm for machine learning has not caught up with the scientific evidence to support the equity and accuracy of diagnostic and therapeutic algorithms. This proposal examines algorithmic biases, including those related to race, gender and socioeconomic status, and accuracy, including the paucity of prospective studies and lack of multisite validation. We then suggest solutions to these problems. We describe the Mayo Clinic, Duke University, Change Healthcare project that is evaluating 35.1 billion healthcare records for bias. And we propose ‘Ingredients’ style labels and an AI evaluation/testing system to help clinicians judge the merits of products and services that include algorithms. Said testing would include input data sources and types, dataset population composition, algorithm validation techniques, bias assessment evaluation and performance metrics. BMJ Publishing Group 2022-04-10 /pmc/articles/PMC9003600/ /pubmed/35410952 http://dx.doi.org/10.1136/bmjhci-2021-100423 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Review Cerrato, Paul Halamka, John Pencina, Michael A proposal for developing a platform that evaluates algorithmic equity and accuracy |
title | A proposal for developing a platform that evaluates algorithmic equity and accuracy |
title_full | A proposal for developing a platform that evaluates algorithmic equity and accuracy |
title_fullStr | A proposal for developing a platform that evaluates algorithmic equity and accuracy |
title_full_unstemmed | A proposal for developing a platform that evaluates algorithmic equity and accuracy |
title_short | A proposal for developing a platform that evaluates algorithmic equity and accuracy |
title_sort | proposal for developing a platform that evaluates algorithmic equity and accuracy |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003600/ https://www.ncbi.nlm.nih.gov/pubmed/35410952 http://dx.doi.org/10.1136/bmjhci-2021-100423 |
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