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Cited by 196Cutaneous Melanoma Detection and ManagementArtificial Intelligence in Healthcare and EducationAI in cancer detection

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Open access
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.·2020·Frontiers in Medicine
DOI 10.3389/fmed.2020.00233PMC PMC7326111
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Abstract

Background: Artificial intelligence 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. 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 towards artificial intelligence in general as well as their attitudes towards 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 people (154 with melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.913 – 0.967] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.846 – 0.919] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.350 – 0.462] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.917 – 0.969 to its use as assistant 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 towards the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.

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Crossref · 10.3389/fmed.2020.00233

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Figure 1 General attitude toward the use of Artificial Intelligence (AI) in the medical field. Bar chart shows distribution of participants' answers regarding their awareness of and attitude toward AI in medical use, provided that the AI-based system can distinguish well between images of harmless moles and melanoma.
Figure 1 General attitude toward the use of Artificial Intelligence (AI) in the medical field. Bar chart shows distribution of participants' answers regarding their awareness of and attitude toward AI in medical use, provided that the AI-based system can distinguish well between images of harmless moles and melanoma.CC BY · PubMed Central
Figure 2 Confidence in AI. Bar chart shows the frequency of participants' answers.
Figure 2 Confidence in AI. Bar chart shows the frequency of participants' answers.CC BY · PubMed Central
Figure 3 Attitudes toward different application scenarios. (A) Pie chart shows the percentages of all participants' preferences. (B) Pie chart shows the percentages of melanoma patients' preferences. (C) Pie chart shows the percentages of preferences of participants without previous history of melanoma.
Figure 3 Attitudes toward different application scenarios. (A) Pie chart shows the percentages of all participants' preferences. (B) Pie chart shows the percentages of melanoma patients' preferences. (C) Pie chart shows the percentages of preferences of participants without previous history of melanoma.CC BY · PubMed Central
Figure 4 Attitudes toward patients' participation. Pie chart shows the percentages of participants' preferences.
Figure 4 Attitudes toward patients' participation. Pie chart shows the percentages of participants' preferences.CC BY · PubMed Central

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Authors
Jutzi, T. B., Krieghoff-Henning, E. I., Holland-Letz, T., Utikal, J. S., Hauschild, A., Schadendorf, D., Sondermann, W., Fröhling, S., Hekler, A., Schmitt, M., Maron, R. C., & Brinker, T. J.
Affiliations
  • German Cancer Research Center (DE)
  • University Hospital Schleswig-Holstein (DE)
  • Essen University Hospital (DE)
Corresponding author
Brinker, Titus J.
ORCID
Venue
Frontiers in Medicine
Publisher
Frontiers Media SA
Published
2020-06-02
Volume
7
Pages
233-233
ISSN
2296-858X (ISSN-L)
DOI
10.3389/fmed.2020.00233
PMC
PMC7326111
OpenAlex
W3033209670
Cited by
196
References
25
Citations by year
2026: 31 · 2025: 44 · 2024: 47 · 2023: 26 · 2022: 30 · 2021: 17 · 2020: 1
FWCI
6.68
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
artificial intelligence, melanoma, patient attitudes, patient perspectives, early detection, data protection, patient trust, sociodemographic factors

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