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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.
Clinical and Experimental Dermatology
|August 11, 2026
Summary
AI tools show promise for atopic dermatitis (AD) management, but limited validation and real-world testing hinder widespread clinical use. Improving dataset diversity and rigorous evaluation are crucial for successful implementation of these digital health solutions.
Area of Science:
- Digital health
- Artificial Intelligence
- Dermatology
Background:
- Atopic dermatitis (AD) significantly impacts children and adults, necessitating innovative diagnostic and management strategies.
- Digital health technologies offer potential solutions, but their clinical readiness requires thorough assessment.
- Existing approaches to AD management face challenges in addressing the condition's fluctuating nature and impact on quality of life.
Purpose of the Study:
- To systematically review artificial intelligence (AI)-driven digital tools for atopic dermatitis (AD).
- To categorize the functionalities of these AI tools, evaluate their methodologies, and identify implementation barriers and opportunities.
- To map the landscape of AI applications in AD diagnosis and management.
Main Methods:
- A comprehensive literature search was performed across major databases (MEDLINE, Embase, Web of Science, Scopus) up to December 2024.
- Studies focusing on diagnostic, symptom-tracking, predictive, teledermatology, or language-based AI tools for AD were included.
- Methodological quality was assessed using a standardized framework, with data synthesized descriptively.
Main Results:
- 52 studies met inclusion criteria, with AI applications including diagnostic tools (32), predictive models (16), and symptom-tracking tools (8).
- While methodological transparency and data partitioning were high, external validation (21%), code availability (21%), and diverse skin color reporting (27%) were limited.
- Many AI diagnostic models, particularly Convolutional Neural Network (CNN)-based ones, achieved high accuracy (>90%), but few were tested in real-world clinical settings.
Conclusions:
- AI-driven digital tools demonstrate significant potential for improving atopic dermatitis (AD) care.
- Clinical translation is constrained by insufficient validation, limited diversity in training data (especially skin types), and a lack of real-world evaluation.
- Future efforts must focus on enhancing methodological rigor, ensuring representative datasets, and conducting robust clinical trials for effective implementation.
