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Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed...

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Autores principales: Jutzi, Tanja B., Krieghoff-Henning, Eva I., Holland-Letz, Tim, Utikal, Jochen Sven, Hauschild, Axel, Schadendorf, Dirk, Sondermann, Wiebke, Fröhling, Stefan, Hekler, Achim, Schmitt, Max, Maron, Roman C., Brinker, Titus J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326111/
https://www.ncbi.nlm.nih.gov/pubmed/32671078
http://dx.doi.org/10.3389/fmed.2020.00233
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author Jutzi, Tanja B.
Krieghoff-Henning, Eva I.
Holland-Letz, Tim
Utikal, Jochen Sven
Hauschild, Axel
Schadendorf, Dirk
Sondermann, Wiebke
Fröhling, Stefan
Hekler, Achim
Schmitt, Max
Maron, Roman C.
Brinker, Titus J.
author_facet Jutzi, Tanja B.
Krieghoff-Henning, Eva I.
Holland-Letz, Tim
Utikal, Jochen Sven
Hauschild, Axel
Schadendorf, Dirk
Sondermann, Wiebke
Fröhling, Stefan
Hekler, Achim
Schmitt, Max
Maron, Roman C.
Brinker, Titus J.
author_sort Jutzi, Tanja B.
collection PubMed
description Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91–0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85–0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35–0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92–0.97] to its use as assistance system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.
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spelling pubmed-73261112020-07-14 Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective Jutzi, Tanja B. Krieghoff-Henning, Eva I. Holland-Letz, Tim Utikal, Jochen Sven Hauschild, Axel Schadendorf, Dirk Sondermann, Wiebke Fröhling, Stefan Hekler, Achim Schmitt, Max Maron, Roman C. Brinker, Titus J. Front Med (Lausanne) Medicine Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma. Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91–0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85–0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35–0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92–0.97] to its use as assistance system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians. Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system. Frontiers Media S.A. 2020-06-02 /pmc/articles/PMC7326111/ /pubmed/32671078 http://dx.doi.org/10.3389/fmed.2020.00233 Text en Copyright © 2020 Jutzi, Krieghoff-Henning, Holland-Letz, Utikal, Hauschild, Schadendorf, Sondermann, Fröhling, Hekler, Schmitt, Maron and Brinker. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Jutzi, Tanja B.
Krieghoff-Henning, Eva I.
Holland-Letz, Tim
Utikal, Jochen Sven
Hauschild, Axel
Schadendorf, Dirk
Sondermann, Wiebke
Fröhling, Stefan
Hekler, Achim
Schmitt, Max
Maron, Roman C.
Brinker, Titus J.
Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
title Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
title_full Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
title_fullStr Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
title_full_unstemmed Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
title_short Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
title_sort artificial intelligence in skin cancer diagnostics: the patients' perspective
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326111/
https://www.ncbi.nlm.nih.gov/pubmed/32671078
http://dx.doi.org/10.3389/fmed.2020.00233
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