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AI-Driven Digital Tools for Atopic Dermatitis: A Scoping Review
Alphonsus Yip1,2, Karen Poole3, Suzanne H Keddie2
1Guy's and St Thomas' NHS Foundation Trust (GSTT), London, UK.
Background:
Atopic dermatitis (AD) affects around 20% of children and up to 10% of adults. Its fluctuating course, severe pruritus, and impact on sleep, mental health, and daily functioning highlight the need for innovative approaches to diagnosis and disease management. Digital health technologies have rapidly expanded to fill this gap, yet their clinical readiness remains uncertain.
Objectives:
This scoping review aims to map AI-driven digital tools for AD, classify their functionalities, assess methodologies, and identify challenges and opportunities for real-world implementation.
Methods:
A comprehensive search of MEDLINE (Ovid), Embase (Ovid), Web of Science, and Scopus was conducted from database inception to December 2024 using controlled vocabulary and free-text terms related to "atopic dermatitis," "eczema," "artificial intelligence," "digital applications," and "digital tools." Two reviewers independently screened studies and extracted data, with conflicts resolved by a senior reviewer. Eligible studies included primary research on diagnostic, symptom-tracking, predictive, teledermatology, or language-based tools. Methodological quality was assessed using a 20-item standardised framework. Descriptive statistics and summary tables were used to synthesise findings.
Results:
52 studies met inclusion criteria with some having multiple applications: diagnostic tools (n=32), symptom-tracking tools (n=8), predictive models (n=16), teledermatology tools (n=3), and Natural Language Processing/Large Language Models (NLP/LLM)-based applications (n=2). While most studies reported methodological transparency (96%) and data partitioning for model development and evaluation (96%); external validation (21%), code availability (21%), skin colour reporting (27%) and multi-expert labelling (38%) were limited. Despite several Convolutional Neural Network (CNN) based diagnostic models achieving >90% accuracy, few tools underwent real-world testing or clinical integration.
Conclusions:
AI-driven tools for AD show strong promise. However, limited validation, insufficient amounts of variation in skin type in source data, and real-world evaluation restrict clinical translation. Collaborative efforts to strengthen methodologies, improve dataset representativeness, and evaluate tools in clinical settings are essential for effective implementation.
