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Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions

BACKGROUND: Artificial intelligence (AI) is being increasingly adopted in the health care industry for administrative tasks, patient care operations, and medical research. OBJECTIVE: We aimed to examine health care workers’ opinions about the adoption and implementation of AI-powered technology in t...

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Autor principal: Nitiéma, Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919460/
https://www.ncbi.nlm.nih.gov/pubmed/36584303
http://dx.doi.org/10.2196/41138
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author Nitiéma, Pascal
author_facet Nitiéma, Pascal
author_sort Nitiéma, Pascal
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description BACKGROUND: Artificial intelligence (AI) is being increasingly adopted in the health care industry for administrative tasks, patient care operations, and medical research. OBJECTIVE: We aimed to examine health care workers’ opinions about the adoption and implementation of AI-powered technology in the health care industry. METHODS: Data were comments about AI posted on a web-based forum by 905 health care professionals from at least 77 countries, from May 2013 to October 2021. Structural topic modeling was used to identify the topics of discussion, and hierarchical clustering was performed to determine how these topics cluster into different groups. RESULTS: Overall, 12 topics were identified from the collected comments. These comments clustered into 2 groups: impact of AI on health care system and practice and AI as a tool for disease screening, diagnosis, and treatment. Topics associated with negative sentiments included concerns about AI replacing human workers, impact of AI on traditional medical diagnostic procedures (ie, patient history and physical examination), accuracy of the algorithm, and entry of IT companies into the health care industry. Concerns about the legal liability for using AI in treating patients were also discussed. Positive topics about AI included the opportunity offered by the technology for improving the accuracy of image-based diagnosis and for enhancing personalized medicine. CONCLUSIONS: The adoption and implementation of AI applications in the health care industry are eliciting both enthusiasm and concerns about patient care quality and the future of health care professions. The successful implementation of AI-powered technologies requires the involvement of all stakeholders, including patients, health care organization workers, health insurance companies, and government regulatory agencies.
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spelling pubmed-99194602023-02-12 Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions Nitiéma, Pascal J Med Internet Res Original Paper BACKGROUND: Artificial intelligence (AI) is being increasingly adopted in the health care industry for administrative tasks, patient care operations, and medical research. OBJECTIVE: We aimed to examine health care workers’ opinions about the adoption and implementation of AI-powered technology in the health care industry. METHODS: Data were comments about AI posted on a web-based forum by 905 health care professionals from at least 77 countries, from May 2013 to October 2021. Structural topic modeling was used to identify the topics of discussion, and hierarchical clustering was performed to determine how these topics cluster into different groups. RESULTS: Overall, 12 topics were identified from the collected comments. These comments clustered into 2 groups: impact of AI on health care system and practice and AI as a tool for disease screening, diagnosis, and treatment. Topics associated with negative sentiments included concerns about AI replacing human workers, impact of AI on traditional medical diagnostic procedures (ie, patient history and physical examination), accuracy of the algorithm, and entry of IT companies into the health care industry. Concerns about the legal liability for using AI in treating patients were also discussed. Positive topics about AI included the opportunity offered by the technology for improving the accuracy of image-based diagnosis and for enhancing personalized medicine. CONCLUSIONS: The adoption and implementation of AI applications in the health care industry are eliciting both enthusiasm and concerns about patient care quality and the future of health care professions. The successful implementation of AI-powered technologies requires the involvement of all stakeholders, including patients, health care organization workers, health insurance companies, and government regulatory agencies. JMIR Publications 2023-01-27 /pmc/articles/PMC9919460/ /pubmed/36584303 http://dx.doi.org/10.2196/41138 Text en ©Pascal Nitiéma. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.01.2023. 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 https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Nitiéma, Pascal
Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions
title Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions
title_full Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions
title_fullStr Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions
title_full_unstemmed Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions
title_short Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions
title_sort artificial intelligence in medicine: text mining of health care workers’ opinions
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919460/
https://www.ncbi.nlm.nih.gov/pubmed/36584303
http://dx.doi.org/10.2196/41138
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