The DOI matches its Crossref record; 98 of the 99 references it cites were found in a registry or an index.
Artificial Intelligence Applications in Dermatology: Where Do We Stand?
Open accessAbstract
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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“Currently, most of the decisions made by AI-based CDSS cannot be interpreted in a transparent way potentially limiting the uptake, trust, and usability of these systems in practice [20,133].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [133]
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Metadata
- 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.
- Venue
- Frontiers in Medicine
- Publisher
- Frontiers Media SA
- Published
- 2020-03-31
- Volume
- 7
- Pages
- 100-100
- ISSN
- 2296-858X (ISSN-L)
- 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
- 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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