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General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study

BACKGROUND: General practitioners (GPs) care for a large number of patients with various diseases in very short timeframes under high uncertainty. Thus, systems enabled by artificial intelligence (AI) are promising and time-saving solutions that may increase the quality of care. OBJECTIVE: This stud...

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Autores principales: Buck, Christoph, Doctor, Eileen, Hennrich, Jasmin, Jöhnk, Jan, Eymann, Torsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8832268/
https://www.ncbi.nlm.nih.gov/pubmed/35084342
http://dx.doi.org/10.2196/28916
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author Buck, Christoph
Doctor, Eileen
Hennrich, Jasmin
Jöhnk, Jan
Eymann, Torsten
author_facet Buck, Christoph
Doctor, Eileen
Hennrich, Jasmin
Jöhnk, Jan
Eymann, Torsten
author_sort Buck, Christoph
collection PubMed
description BACKGROUND: General practitioners (GPs) care for a large number of patients with various diseases in very short timeframes under high uncertainty. Thus, systems enabled by artificial intelligence (AI) are promising and time-saving solutions that may increase the quality of care. OBJECTIVE: This study aims to understand GPs’ attitudes toward AI-enabled systems in medical diagnosis. METHODS: We interviewed 18 GPs from Germany between March 2020 and May 2020 to identify determinants of GPs’ attitudes toward AI-based systems in diagnosis. By analyzing the interview transcripts, we identified 307 open codes, which we then further structured to derive relevant attitude determinants. RESULTS: We merged the open codes into 21 concepts and finally into five categories: concerns, expectations, environmental influences, individual characteristics, and minimum requirements of AI-enabled systems. Concerns included all doubts and fears of the participants regarding AI-enabled systems. Expectations reflected GPs’ thoughts and beliefs about expected benefits and limitations of AI-enabled systems in terms of GP care. Environmental influences included influences resulting from an evolving working environment, key stakeholders’ perspectives and opinions, the available information technology hardware and software resources, and the media environment. Individual characteristics were determinants that describe a physician as a person, including character traits, demographic characteristics, and knowledge. In addition, the interviews also revealed the minimum requirements of AI-enabled systems, which were preconditions that must be met for GPs to contemplate using AI-enabled systems. Moreover, we identified relationships among these categories, which we conflate in our proposed model. CONCLUSIONS: This study provides a thorough understanding of the perspective of future users of AI-enabled systems in primary care and lays the foundation for successful market penetration. We contribute to the research stream of analyzing and designing AI-enabled systems and the literature on attitudes toward technology and practice by fostering the understanding of GPs and their attitudes toward such systems. Our findings provide relevant information to technology developers, policymakers, and stakeholder institutions of GP care.
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spelling pubmed-88322682022-03-07 General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study Buck, Christoph Doctor, Eileen Hennrich, Jasmin Jöhnk, Jan Eymann, Torsten J Med Internet Res Original Paper BACKGROUND: General practitioners (GPs) care for a large number of patients with various diseases in very short timeframes under high uncertainty. Thus, systems enabled by artificial intelligence (AI) are promising and time-saving solutions that may increase the quality of care. OBJECTIVE: This study aims to understand GPs’ attitudes toward AI-enabled systems in medical diagnosis. METHODS: We interviewed 18 GPs from Germany between March 2020 and May 2020 to identify determinants of GPs’ attitudes toward AI-based systems in diagnosis. By analyzing the interview transcripts, we identified 307 open codes, which we then further structured to derive relevant attitude determinants. RESULTS: We merged the open codes into 21 concepts and finally into five categories: concerns, expectations, environmental influences, individual characteristics, and minimum requirements of AI-enabled systems. Concerns included all doubts and fears of the participants regarding AI-enabled systems. Expectations reflected GPs’ thoughts and beliefs about expected benefits and limitations of AI-enabled systems in terms of GP care. Environmental influences included influences resulting from an evolving working environment, key stakeholders’ perspectives and opinions, the available information technology hardware and software resources, and the media environment. Individual characteristics were determinants that describe a physician as a person, including character traits, demographic characteristics, and knowledge. In addition, the interviews also revealed the minimum requirements of AI-enabled systems, which were preconditions that must be met for GPs to contemplate using AI-enabled systems. Moreover, we identified relationships among these categories, which we conflate in our proposed model. CONCLUSIONS: This study provides a thorough understanding of the perspective of future users of AI-enabled systems in primary care and lays the foundation for successful market penetration. We contribute to the research stream of analyzing and designing AI-enabled systems and the literature on attitudes toward technology and practice by fostering the understanding of GPs and their attitudes toward such systems. Our findings provide relevant information to technology developers, policymakers, and stakeholder institutions of GP care. JMIR Publications 2022-01-27 /pmc/articles/PMC8832268/ /pubmed/35084342 http://dx.doi.org/10.2196/28916 Text en ©Christoph Buck, Eileen Doctor, Jasmin Hennrich, Jan Jöhnk, Torsten Eymann. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.01.2022. 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
Buck, Christoph
Doctor, Eileen
Hennrich, Jasmin
Jöhnk, Jan
Eymann, Torsten
General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study
title General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study
title_full General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study
title_fullStr General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study
title_full_unstemmed General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study
title_short General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study
title_sort general practitioners’ attitudes toward artificial intelligence–enabled systems: interview study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8832268/
https://www.ncbi.nlm.nih.gov/pubmed/35084342
http://dx.doi.org/10.2196/28916
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