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Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?

In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly, the perfor...

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Detalles Bibliográficos
Autor principal: Lee, Joon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7481865/
https://www.ncbi.nlm.nih.gov/pubmed/32845249
http://dx.doi.org/10.2196/19918
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author Lee, Joon
author_facet Lee, Joon
author_sort Lee, Joon
collection PubMed
description In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly, the performance of machine learning–based patient outcome prediction models has rarely been compared with that of human clinicians in the literature. Human intuition and insight may be sources of underused predictive information that AI will not be able to identify in electronic data. Both human and AI predictions should be investigated together with the aim of achieving a human-AI symbiosis that synergistically and complementarily combines AI with the predictive abilities of clinicians.
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spelling pubmed-74818652020-10-02 Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes? Lee, Joon J Med Internet Res Viewpoint In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly, the performance of machine learning–based patient outcome prediction models has rarely been compared with that of human clinicians in the literature. Human intuition and insight may be sources of underused predictive information that AI will not be able to identify in electronic data. Both human and AI predictions should be investigated together with the aim of achieving a human-AI symbiosis that synergistically and complementarily combines AI with the predictive abilities of clinicians. JMIR Publications 2020-08-26 /pmc/articles/PMC7481865/ /pubmed/32845249 http://dx.doi.org/10.2196/19918 Text en ©Joon Lee. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 26.08.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Viewpoint
Lee, Joon
Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?
title Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?
title_full Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?
title_fullStr Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?
title_full_unstemmed Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?
title_short Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?
title_sort is artificial intelligence better than human clinicians in predicting patient outcomes?
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7481865/
https://www.ncbi.nlm.nih.gov/pubmed/32845249
http://dx.doi.org/10.2196/19918
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