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. 2024 Dec;38(12):2225-2234.
doi: 10.1111/jdv.20286. Epub 2024 Aug 16.

Natural language processing in dermatology: A systematic literature review and state of the art

Alessia Paganelli  1 , Marco Spadafora  2   3 , Cristian Navarrete-Dechent  4 , Stefania Guida  5 , Giovanni Pellacani  6 , Caterina Longo  2   3
Affiliations

Natural language processing in dermatology: A systematic literature review and state of the art

Alessia Paganelli et al. J Eur Acad Dermatol Venereol. 2024 Dec.

Abstract

Background: Natural Language Processing (NLP) is a field of both computational linguistics and artificial intelligence (AI) dedicated to analysis and interpretation of human language.

Objectives: This systematic review aims at exploring all the possible applications of NLP techniques in the dermatological setting.

Methods: Extensive search on 'natural language processing' and 'dermatology' was performed on MEDLINE and Scopus electronic databases. Only journal articles with full text electronically available and English translation were considered. The PICO (Population, Intervention or exposure, Comparison, Outcome) algorithm was applied to our study protocol.

Results: Natural Language Processing (NLP) techniques have been utilized across various dermatological domains, including atopic dermatitis, acne/rosacea, skin infections, non-melanoma skin cancers (NMSCs), melanoma and skincare. There is versatility of NLP in data extraction from diverse sources such as electronic health records (EHRs), social media platforms and online forums. We found extensive utilization of NLP techniques across diverse dermatological domains, showcasing its potential in extracting valuable insights from various sources and informing diagnosis, treatment optimization, patient preferences and unmet needs in dermatological research and clinical practice.

Conclusions: While NLP shows promise in enhancing dermatological research and clinical practice, challenges such as data quality, ambiguity, lack of standardization and privacy concerns necessitate careful consideration. Collaborative efforts between dermatologists, data scientists and ethicists are essential for addressing these challenges and maximizing the potential of NLP in dermatology.

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Conflict of interest statement

None declared.

Figures

FIGURE 1
FIGURE 1
Workflow diagram describing the systematic selection of studies for inclusion in the present review (PRISMA flowchart).
FIGURE 2
FIGURE 2
Graphical representation of the published papers on the use of NLP in the dermatological setting overtime according to our study inclusion criteria.
See this image and copyright information in PMC

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