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Evaluating artificial intelligence in medicine: phases of clinical research

Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need for real-world evaluations for effectiveness and unintended consequences. The complexity of healthcare, compounded by the user- and context-dependent nature of AI applications, calls for a multifaceted...

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Detalles Bibliográficos
Autores principales: Park, Yoonyoung, Jackson, Gretchen Purcell, Foreman, Morgan A, Gruen, Daniel, Hu, Jianying, Das, Amar K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660958/
https://www.ncbi.nlm.nih.gov/pubmed/33215066
http://dx.doi.org/10.1093/jamiaopen/ooaa033
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author Park, Yoonyoung
Jackson, Gretchen Purcell
Foreman, Morgan A
Gruen, Daniel
Hu, Jianying
Das, Amar K
author_facet Park, Yoonyoung
Jackson, Gretchen Purcell
Foreman, Morgan A
Gruen, Daniel
Hu, Jianying
Das, Amar K
author_sort Park, Yoonyoung
collection PubMed
description Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need for real-world evaluations for effectiveness and unintended consequences. The complexity of healthcare, compounded by the user- and context-dependent nature of AI applications, calls for a multifaceted approach beyond traditional in silico evaluation of AI. We propose an interdisciplinary, phased research framework for evaluation of AI implementations in healthcare. We draw analogies to and highlight differences from the clinical trial phases for drugs and medical devices, and we present study design and methodological guidance for each stage.
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spelling pubmed-76609582020-11-18 Evaluating artificial intelligence in medicine: phases of clinical research Park, Yoonyoung Jackson, Gretchen Purcell Foreman, Morgan A Gruen, Daniel Hu, Jianying Das, Amar K JAMIA Open Perspective Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need for real-world evaluations for effectiveness and unintended consequences. The complexity of healthcare, compounded by the user- and context-dependent nature of AI applications, calls for a multifaceted approach beyond traditional in silico evaluation of AI. We propose an interdisciplinary, phased research framework for evaluation of AI implementations in healthcare. We draw analogies to and highlight differences from the clinical trial phases for drugs and medical devices, and we present study design and methodological guidance for each stage. Oxford University Press 2020-09-08 /pmc/articles/PMC7660958/ /pubmed/33215066 http://dx.doi.org/10.1093/jamiaopen/ooaa033 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Perspective
Park, Yoonyoung
Jackson, Gretchen Purcell
Foreman, Morgan A
Gruen, Daniel
Hu, Jianying
Das, Amar K
Evaluating artificial intelligence in medicine: phases of clinical research
title Evaluating artificial intelligence in medicine: phases of clinical research
title_full Evaluating artificial intelligence in medicine: phases of clinical research
title_fullStr Evaluating artificial intelligence in medicine: phases of clinical research
title_full_unstemmed Evaluating artificial intelligence in medicine: phases of clinical research
title_short Evaluating artificial intelligence in medicine: phases of clinical research
title_sort evaluating artificial intelligence in medicine: phases of clinical research
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660958/
https://www.ncbi.nlm.nih.gov/pubmed/33215066
http://dx.doi.org/10.1093/jamiaopen/ooaa033
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