Journal of the European Academy of Dermatology and Venereology : JEADV · 2024
Natural language processing in dermatology: A systematic literature review and state of the art.
СтатьяСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Paganelli Alessia, Spadafora Marco, Navarrete-Dechent Cristian, Guida Stefania, Pellacani Giovanni, Longo Caterina
Аннотация
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.
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.