Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
Open accessAbstract
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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Previous empirical studies on patients' acceptance or trust in AI are somewhat conflicting.
“The results of previous empirical studies on patients’ acceptance or trust in AI are somewhat conflicting. Some studies have shown that participants are more accepting of AI if it is proven to be more accurate than providers26,29 or if the use of AI fits societal and cultural norms.35,36 However, other studies have found that trust in AI did not increase when participants were informed that the AI”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [29]
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AI in medical diagnosis: A contextualised study of patient motivations and concerns
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Editorial: The Emerging Role of Artificial Intelligence in Dermatology
Mahmood, F., Bendayan, S., Ghazawi, F. M., & Litvinov, I. V. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.751649 · Cited by 19 · see citing works
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Comparing preferences for skin cancer screening: AI-enabled app vs dermatologist
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Skin Cancer Disease Detection Using MCD-GRU: A Deep Learning Approach
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Artificial intelligence in Dermatopathology
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Key Information Influencing Patient Decision-Making About AI in Health Care: Survey Experiment Study
Zhu, X., Stroud, A. M., Minteer, S. A., Yoo, D. W., Ridgeway, J. L., Mooghali, M., Miller, J. E., & Barry, B. A. (2025) · Journal of Medical Internet Research · doi:10.2196/75615 · Cited by 18 · see citing works
Gassner, M., García, J. B., Tanadini‐Lang, S., Bertoldo, F., Fröhlich, F., Gückenberger, M., Haueis, S., Pelzer, C., Reyes, M., Schmithausen, P., et al. (2023) · JMIR Dermatology · doi:10.2196/42129 · Cited by 14 · see citing works
Smart MobiNet: A Deep Learning Approach for Accurate Skin Cancer Diagnosis
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Ho, A., Bavli, I., Mahal, R., & McKeown, M. J. (2023) · AJOB Empirical Bioethics · doi:10.1080/23294515.2023.2274582 · Cited by 12 · see citing works
Basic principles of artificial intelligence in dermatology explained using melanoma
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Transformer-based framework for multi-class segmentation of skin cancer from histopathology images
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Patient Attitudes Toward Artificial Intelligence in Cancer Care: Scoping Review
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Patients’ attitudes toward artificial intelligence (AI) in cancer care: A scoping review protocol
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Perspective Toward Machine Learning Implementation in Pediatric Medicine: Mixed Methods Study
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Patient Perceptions of Artificial Intelligence and Telemedicine in Dermatology: Narrative Review
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Artificial Intelligence in Skin Cancer: Diagnosis and Therapy
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A survey of gender-based barriers and misconceptions in surgery
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The 100 most influential papers in medical artificial intelligence; a bibliometric analysis
Zahoor, F., Abdullah, M., Tahir, M. W., & Islam, A. M. (2024) · Journal of the Pakistan Medical Association · doi:10.47391/jpma.10064 · Cited by 8 · see citing works
Qurban, Q., & Cassidy, L. (2024) · SAGE Open Medicine · doi:10.1177/20503121241274197 · Cited by 7 · see citing works
Krisnan, L., Masrom, M. B., Yahya, Y., & Krishnan, M. (2025) · Asian Journal of Social Health and Behavior · doi:10.4103/shb.shb_92_24 · Cited by 5 · see citing works
Springer, F., Hambsch, P. K., Mehnert-Theuerkauf, A., & Nicolay, N. H. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1546221 · Cited by 5 · see citing works
Zhang, M., Chu, R., Liu, C., Zhang, S., & Ren, X. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1684035 · Cited by 7 · see citing works
- 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)
- 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
- gold
- doi.org
- 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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