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An Open Science Approach to Artificial Intelligence in Healthcare: A Contribution from the International Medical Informatics Association Open Source Working Group

Objectives : Artificial Intelligence (AI) offers significant potential for improving healthcare. This paper discusses how an “open science” approach to AI tool development, data sharing, education, and research can support the clinical adoption of AI systems. Method : In response to the call for par...

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Autores principales: Paton, Chris, Kobayashi, Shinji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2019
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697543/
https://www.ncbi.nlm.nih.gov/pubmed/31022753
http://dx.doi.org/10.1055/s-0039-1677898
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author Paton, Chris
Kobayashi, Shinji
author_facet Paton, Chris
Kobayashi, Shinji
author_sort Paton, Chris
collection PubMed
description Objectives : Artificial Intelligence (AI) offers significant potential for improving healthcare. This paper discusses how an “open science” approach to AI tool development, data sharing, education, and research can support the clinical adoption of AI systems. Method : In response to the call for participation for the 2019 International Medical Informatics Association (IMIA) Yearbook theme issue on AI in healthcare, the IMIA Open Source Working Group conducted a rapid review of recent literature relating to open science and AI in healthcare and discussed how an open science approach could help overcome concerns about the adoption of new AI technology in healthcare settings. Results : The recent literature reveals that open science approaches to AI system development are well established. The ecosystem of software development, data sharing, education, and research in the AI community has, in general, adopted an open science ethos that has driven much of the recent innovation and adoption of new AI techniques. However, within the healthcare domain, adoption may be inhibited by the use of “black-box” AI systems, where only the inputs and outputs of those systems are understood, and clinical effectiveness and implementation studies are missing. Conclusions : As AI-based data analysis and clinical decision support systems begin to be implemented in healthcare systems around the world, further openness of clinical effectiveness and mechanisms of action may be required by safety-conscious healthcare policy-makers to ensure they are clinically effective in real world use.
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spelling pubmed-66975432019-08-19 An Open Science Approach to Artificial Intelligence in Healthcare: A Contribution from the International Medical Informatics Association Open Source Working Group Paton, Chris Kobayashi, Shinji Yearb Med Inform Objectives : Artificial Intelligence (AI) offers significant potential for improving healthcare. This paper discusses how an “open science” approach to AI tool development, data sharing, education, and research can support the clinical adoption of AI systems. Method : In response to the call for participation for the 2019 International Medical Informatics Association (IMIA) Yearbook theme issue on AI in healthcare, the IMIA Open Source Working Group conducted a rapid review of recent literature relating to open science and AI in healthcare and discussed how an open science approach could help overcome concerns about the adoption of new AI technology in healthcare settings. Results : The recent literature reveals that open science approaches to AI system development are well established. The ecosystem of software development, data sharing, education, and research in the AI community has, in general, adopted an open science ethos that has driven much of the recent innovation and adoption of new AI techniques. However, within the healthcare domain, adoption may be inhibited by the use of “black-box” AI systems, where only the inputs and outputs of those systems are understood, and clinical effectiveness and implementation studies are missing. Conclusions : As AI-based data analysis and clinical decision support systems begin to be implemented in healthcare systems around the world, further openness of clinical effectiveness and mechanisms of action may be required by safety-conscious healthcare policy-makers to ensure they are clinically effective in real world use. Georg Thieme Verlag KG 2019-08 2019-04-25 /pmc/articles/PMC6697543/ /pubmed/31022753 http://dx.doi.org/10.1055/s-0039-1677898 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.
spellingShingle Paton, Chris
Kobayashi, Shinji
An Open Science Approach to Artificial Intelligence in Healthcare: A Contribution from the International Medical Informatics Association Open Source Working Group
title An Open Science Approach to Artificial Intelligence in Healthcare: A Contribution from the International Medical Informatics Association Open Source Working Group
title_full An Open Science Approach to Artificial Intelligence in Healthcare: A Contribution from the International Medical Informatics Association Open Source Working Group
title_fullStr An Open Science Approach to Artificial Intelligence in Healthcare: A Contribution from the International Medical Informatics Association Open Source Working Group
title_full_unstemmed An Open Science Approach to Artificial Intelligence in Healthcare: A Contribution from the International Medical Informatics Association Open Source Working Group
title_short An Open Science Approach to Artificial Intelligence in Healthcare: A Contribution from the International Medical Informatics Association Open Source Working Group
title_sort open science approach to artificial intelligence in healthcare: a contribution from the international medical informatics association open source working group
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697543/
https://www.ncbi.nlm.nih.gov/pubmed/31022753
http://dx.doi.org/10.1055/s-0039-1677898
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