Towards successful implementation of artificial intelligence in skin cancer care: a qualitative study exploring the views of dermatologists and general practitioners
Open accessAbstract
Recent studies show promising potential for artificial intelligence (AI) to assist healthcare providers (HCPs) in skin cancer care. The aim of this study is to explore the views of dermatologists and general practitioners (GPs) regarding the successful implementation of AI when assisting HCPs in skin cancer care. We performed a qualitative focus group study, consisting of six focus groups with 16 dermatologists and 17 GPs, varying in prior knowledge and experience with AI, gender, and age. An in-depth inductive thematic content analysis was deployed. Perceived benefits, barriers, and preconditions were identified as main themes. Dermatologists and GPs perceive substantial benefits of AI, particularly an improved health outcome and care pathway between primary and secondary care. Doubts about accuracy, risk of health inequalities, and fear of replacement were among the most stressed barriers. Essential preconditions included adequate algorithm content, sufficient usability, and accessibility of AI. In conclusion, dermatologists and GPs perceive significant benefits from implementing AI in skin cancer care. However, to successfully implement AI, key barriers need to be addressed. Efforts should focus on ensuring algorithm transparency, validation, accessibility for all skin types, and adequate regulation of algorithms. Simultaneously, improving knowledge about AI could reduce the fear of replacement.
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Five studies reported facilitating factors at the individual level that encourage greater knowledge about AI by healthcare professionals, including being familiar with AI, having time to trial the AI application, receiving education and training programs, and conferences and scientific networks.
“Five studies reported on the facilitating factors at the indivudual level that encourage greater knowledge about AI by healthcare professionals, including being familiar with AI [34, 37, 38], having time to trial the AI application in their practice [39] and receiving education and training programs [39] as well as conferences and scientific networks [40] as opportunities to learn about them.”
From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [38]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredMost studies reported benefits relating to better diagnostic quality in either improved sensitivity and specificity or reducing the likelihood of missed diagnoses.
“In terms of medical decision-making in clinical settings, most studies reported on benefits relating to a better diagnostic quality in either improved sensitivity and specificity or reducing the likelihood of missed diagnoses [31, 33, 38, 44, 46, 52, 55, 60–75].”
From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [38]
The Results (Perceived benefits) report that dermatologists and GPs expected AI to improve diagnostic accuracy and lead to fewer missed skin cancer diagnoses. The Strengths and limitations section clarifies that these were mainly views gathered before implementation. This qualitative study does not establish improved sensitivity or specificity, or substantiate the claim that most studies reported such benefits. (LLM, conf=0.88)
Experimental — accuracy not yet measuredHealthcare professionals would prefer to see themselves as retaining overall control of the treatment process and that AI is used as a decision-support and not a replacement, while also acknowledging that AI can serve as a tool for educational purposes and can lead to more self-assurance in their decisions.
“Healthcare professionals would prefer to see themselves as retaining the overall control of the treatment process of their patients and that AI is used as a decision-support and not a replacement [36, 39, 44, 45], while also acknowledging that AI can also serve as a tool for educational purposes [38] and can lead to more self-assurance in their decisions [43].”
From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [38]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredEndorsements from leaders within their fields, academic societies, and trusted experts lend credibility and acceptance to AI technologies.
“Furthermore, endorsements from leaders within their fields, academic societies, and trusted experts lend credibility and acceptance to AI technologies [38, 52].”
From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [38]
In Results > Preconditions for implementation (page unavailable), dermatologists and GPs expected endorsements from national medical societies, and GPs considered positive feedback from other GPs a possible form of validation. The passage does not specifically identify field leaders or trusted experts as endorsers, and its findings concern clinicians considering AI for skin cancer care rather than acceptance of AI technologies generally. (LLM, conf=0.84)
Experimental — accuracy not yet measuredA facilitating factor is that outcomes and clinical effectiveness of AI applications should be evidence-based, ideally relying on large randomized controlled trials to support claims about effectiveness and safety, while the development process itself should be transparent and the AI system used should be explainable and verifiable.
“Hereby, a facilitating factor is that the outcomes and clinical effectivenes of AI applications should be evidence-based, ideally relying on large randomized controlled trials (RCTs) to support claims about its effectiveness and safety [36, 38, 39, 42, 58], while the development process itself should be transparent [63] and the AI system used should be explainable and verifiable [63].”
From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [38]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Many studies reported an expected increase in overall efficiency as a facilitating factor, a reduction in overall workload, or improved time management.
“many studies reported an expected increase in overall efficiency [38, 41, 44, 45, 55, 56, 62, 67, 77–80] as a facilitating factor, a reduction in overall workload [82–84] or improved time management [52, 66].”
Other facilitating factors include the ease of use and user-friendliness of AI technology, ensuring that it is accessible and usable for healthcare professionals.
“Other facilitating factors include the ease of use and user-friendliness of AI technology, ensuring that it is accessible and usable for healthcare professionals [36, 38, 39].”
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Kołodziej, F. M., Lis, M., Komin, Z. J., Liasota, M., Kabat, E. Z., Kozłowska, M. J., Konopka, K., Majchrzak, M. K., Juskowiak, J., & Kwiatkowska, A. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.4(52).2026.6380
Miah, M. S., Tuli, K. N., Rony, M. K. K., Khatun, S., Mousume, M. S. A., Ara, G., Baidya, S., Rahman, T., Misti, J., & Salma, U. (2026) · Advances in Public Health · doi:10.1155/adph/2313046
- Authors
- Sangers, T. E., Wakkee, M., Moolenburgh, F. J., Nijsten, T., & Lugtenberg, M.
- Affiliations
- Erasmus MC (NL)
- Corresponding author
- Lugtenberg, Marjolein
- Venue
- Archives of Dermatological Research
- Published
- 2022-12-07
- Volume
- 315
- Issue
- 5
- Pages
- 1187-1195
- ISSN
- 0003-9187 (ISSN-L)
- 1432-069X
- PMC
- PMC9734890
- OpenAlex
- W4311284839
- Cited by
- 38
- References
- 41
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 7 · 2023: 6
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Archives of Dermatological Research (publishedVersion)
- PubMed Central (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- Research portal (Tilburg University) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Funders
- SkinVision (Unrestricted research grant)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence, AI implementation, dermatologists, general practitioners, perceived benefits, implementation barriers, algorithmic transparency, algorithm validation, health disparities, fear of job replacement, regulation of algorithms
- MeSH terms
- Dermatologists, Artificial Intelligence, Humans, Skin, Skin Neoplasms, General Practitioners
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