The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review
Open accessAbstract
The prevalence of dermatological conditions in primary care, coupled with challenges such as dermatologist shortages and rising consultation costs, highlights the need for innovative solutions. Artificial intelligence (AI) holds promise for improving the diagnostic analysis of skin lesion images, potentially enhancing patient care in primary settings. This systematic review following PRISMA guidelines examined primary studies (2012–2022) assessing AI algorithms’ diagnostic accuracy for skin diseases in primary care. Studies were screened for eligibility based on their availability in the English language and exclusion criteria, with risk of bias evaluated using QUADAS-2. PubMed, Scopus, and Web of Science were searched. Fifteen studies (2019–2022), primarily from Europe and the USA, focusing on diagnostic accuracy were included. Sensitivity ranged from 58% to 96.1%, with accuracies varying from 0.41 to 0.93. AI applications encompassed triage and diagnostic support across diverse skin conditions in primary care settings, involving both patients and primary care professionals. While AI demonstrates potential for enhancing the accuracy of skin disease diagnostics in primary care, further research is imperative to address study heterogeneity and ensure algorithm reliability across diverse populations. Future investigations should prioritise robust dataset development and consider representative patient samples. Overall, AI may improve dermatological diagnosis in primary care, but careful consideration of algorithm limitations and implementation strategies is required.
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CNN and Deep Neural Networks are good alternatives for skin disease diagnosis in primary care settings.
“CNN and Deep Neural Networks are good alternatives for skin disease diagnosis in primary care settings [41].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [41]
The Discussion (page unavailable) says the reviewed algorithms used CNNs and DNNs and that AI improved primary-care professionals’ diagnostic accuracy. But the Conclusions (page unavailable) describe AI as having potential to improve diagnosis, subject to further research and careful implementation; they do not establish these models as good alternatives to existing diagnosis. (LLM, conf=0.82)
Experimental — accuracy not yet measuredOvercoming these challenges requires interdisciplinary collaboration, developing a concrete plan by policymakers and administrators, dedicated healthcare professional education, and ongoing research and development to support effective AI integration into PHC.
“Overcoming these challenges requires interdisciplinary collaboration, developing a concrete plan by policymakers and administrators, dedicated healthcare professional education, and ongoing research and development to support effective AI integration into PHC [30, 37, 39–42].”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [42]
The Conclusions (page not specified) call for further research and careful consideration of implementation strategies for AI-assisted skin-disease diagnosis in primary care. They do not call for interdisciplinary collaboration, a concrete plan by policymakers and administrators, or dedicated healthcare-professional education. (LLM, conf=0.87)
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“CNN and Deep Neural Networks are good alternatives for skin disease diagnosis in primary care settings [41].”
- Authors
- Escalé-Besa, A., Vidal-Alaball, J., Miró Catalina, Q., Gracia, V. H. G., Marin-Gomez, F. X., & Fuster-Casanovas, A.
- Affiliations
- Universitat de Vic - Universitat Central de Catalunya (ES)
- Health Promotion in Rural Areas Research Group, Gerència d’Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, 08242 Manresa, Spain
- Institut Català de la Salut (ES)
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina, 082424 Manresa, Spain
- ORCID
- Venue
- Healthcare
- Publisher
- MDPI AG
- Published
- 2024-06-13
- Volume
- 12
- Issue
- 12
- Pages
- 1192
- ISSN
- 2227-9032 (ISSN-L)
- PMC
- PMC11202856
- OpenAlex
- W4399608418
- Cited by
- 29
- References
- 53
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 1
- FWCI
- 1.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Spanish Society of Family and Community Medicine
- Fundació Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, dermatological diagnosis, primary care, skin imaging, diagnostic accuracy, triage, diagnostic support, dermatologist shortage, algorithm reliability, skin lesion images
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