Cited by 142Cutaneous Melanoma Detection and ManagementArtificial Intelligence in Healthcare and EducationAI in cancer detection

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Artificial Intelligence Applications in Dermatology: Where Do We Stand?

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Gomolin, Arieh, Netchiporouk, Elena, Gniadecki, Robert, Litvinov, Ivan V.·2020·Frontiers in Medicine

DOI 10.3389/fmed.2020.00100PMC PMC7136423

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Abstract

Artificial intelligence (AI) has become a progressively prevalent Research Topic in medicine and is increasingly being applied to dermatology. There is a need to understand this technology's progress to help guide and shape the future for medical care providers and recipients. We reviewed the literature to evaluate the types of publications on the subject, the specific dermatological topics addressed by AI, and the most challenging barriers to its implementation. A substantial number of original articles and commentaries have been published to date and only few detailed reviews exist. Most AI applications focus on differentiating between benign and malignant skin lesions, however; others exist pertaining to ulcers, inflammatory skin diseases, allergen exposure, dermatopathology, and gene expression profiling. Applications commonly analyze and classify images, however, other tools such as risk assessment calculators are becoming increasingly available. Although many applications are technologically feasible, important implementation barriers have been identified including systematic biases, difficulty of standardization, interpretability, and acceptance by physicians and patients alike. This review provides insight into future research needs and possibilities. There is a strong need for clinical investigation in dermatology providing evidence of success overcoming the identified barriers. With these research goals in mind, an appropriate role for AI in dermatology may be achieved in not so distant future.

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See all 142

Authors: Gomolin, A., Netchiporouk, E., Gniadecki, R., & Litvinov, I. V.
Affiliations: McGill University Health Centre (CA)
University of Alberta (CA)
Corresponding author: Gniadecki, Robert
Litvinov, Ivan V.
ORCID: Gomolin, Arieh
Netchiporouk, Elena
Gniadecki, Robert
Litvinov, Ivan V.
Venue: Frontiers in Medicine
Publisher: Frontiers Media SA
Published: 2020-03-31
Volume: 7
Pages: 100-100
ISSN: 2296-858X (ISSN-L)
DOI: 10.3389/fmed.2020.00100
PMC: PMC7136423
OpenAlex: W3014215822
Cited by: 142
References: 99
Citations by year: 2026: 9 · 2025: 31 · 2024: 25 · 2023: 17 · 2022: 26 · 2021: 25 · 2020: 9
FWCI: 2.07
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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, dermatology, skin lesion classification, benign versus malignant skin lesions, skin ulcers, inflammatory skin diseases, allergen exposure, dermatopathology, gene expression profiling, implementation barriers, algorithmic bias, interpretability

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Artificial Intelligence Applications in Dermatology: Where Do We Stand?

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