Digital Health
OVERVIEW OF DIGITAL HEALTH
Digital Health has the potential to enhance psoriasis care and research across the disease continuum, from early diagnosis to long-term management. In recognition of this opportunity, the IPC established a Digital Health Working Group in 2023, chaired by IPC Board Member Andrew Blauvelt and co-chaired by IPC Councilor George Han, to address the expanding scope of digital tools relevant to psoriasis beyond teledermatology.
Digital health encompasses a broad range of digital tools, including telemedicine, mobile health applications, artificial intelligence (AI), skin imaging technologies, wearable devices, and electronic health records. Building on prior IPC initiatives, including the 2023 Telemedicine Working Group publication that outlined opportunities and limitations of telemedicine in psoriasis care, the Digital Health Working Group aims to evaluate how these technologies can be responsibly and effectively integrated into clinical practice and research, with particular emphasis on their role in improving early diagnosis and optimal care initiation, which are central priorities of the IPC. At the same time, the Group seeks to assess risks, limitations, and equity considerations associated with digital health implementation across diverse healthcare systems.
Fast Facts
- Digital Health approaches can support shared decision-making between patients and clinicians.
- Digital tools can enhance research, facilitating data collection and analysis.
- Implementation strategies must account for variability in digital infrastructure and the need to adapt to healthcare system contexts.
- Training and education are necessary to ensure the appropriate use of digital tools by healthcare professionals.
- Ethical considerations must be taken into account when using digital tools to protect sensitive data.
Latest Publications
IPC Resources
RESOURCES FOR PATIENTS
Additional Resources
ARTICLES/OTHER RESOURCES
AI in Psoriatic Disease: Scoping Review. Barlow R, Bewley A, Gkini MA. JMIR Dermatol. 2024;7:e50451. Published 2024 Oct 16. doi:10.2196/50451
A comprehensive overview of current artificial intelligence (AI) applications in psoriatic disease, summarizing advances in diagnosis, disease monitoring, and treatment prediction while highlighting key evidence gaps.
Position Statement of the EADV Artificial Intelligence (AI) Task Force on AI-Assisted Smartphone Apps and Web-Based Services for Skin Disease. Sangers TE, Kittler H, Blum A, et al. J Eur Acad Dermatol Venereol. 2024;38(1):22-30. doi:10.1111/jdv.19521
Summarizes expert recommendations on the safe and responsible use of AI-enabled digital tools in dermatology, emphasizing clinical oversight, validation, and patient protection.
Digital Health Interventions in Dermatology-Mapping Technology and Study Parameters of Systematically Identified Publications. Reinders P, Augustin M, Kirsten N, et al. J Eur Acad Dermatol Venereol. 2023;37(12):2440-2449. doi:10.1111/jdv.19392
Examines the current landscape of digital health tools in dermatology, highlighting the types of technologies studied and the quality of evidence supporting their use.
Digital Health in Dermatology. Kantor J. JAAD Int. 2023;13:139. Published 2023 Aug 20. doi:10.1016/j.jdin.2023.08.008
An overview of emerging digital health modalities in dermatology, including teledermatology, mobile health applications, and AI-driven tools, with discussion of implementation challenges and future opportunities.
Telemedicine: How to Prepare for Your Appointment. American Academy of Dermatology. American Academy of Dermatology website. Last updated August 18, 2023. Available from: https://www.aad.org/public/fad/digital-health/telemedicine/prepare-appointment
A patient-oriented educational resource offering practical guidance on preparing for teledermatology visits, including technology requirements, image capture, and communication best practices.
Machine Learning Model for Predicting Outcomes of Biologic Therapy in Psoriasis. Du AX, Ali Z, Ajgeiy KK, et al. J Am Acad Dermatol. 2023;88(6):1364-1367. doi:10.1016/j.jaad.2022.12.046
Describes the development and performance of a machine learning model to predict biologic treatment outcomes in psoriasis, supporting more personalized and data-driven therapeutic decision-making.
Effects of Online Care on Functional and Psychological Outcomes in Patients with Psoriasis: A Randomized Controlled Trial. Young PM, Chen AY, Ford AR, et al. J Am Acad Dermatol. 2023;88(2):364-370. doi:10.1016/j.jaad.2019.05.089
Reports findings from a randomized controlled trial evaluating the impact of online psoriasis care on functional status and psychological well-being compared with conventional in-person management.
Image-Based Automated Psoriasis Area Severity Index Scoring by Convolutional Neural Networks. Schaap MJ, Cardozo NJ, Patel A, et al. J Eur Acad Dermatol Venereol. 2022;36(1):68-75. doi:10.1111/jdv.17711
Shows how automated image analysis can support objective PASI scoring, with potential to improve consistency in disease severity assessment.
Global Strategy on Digital Health 2020‑-2025. World Health Organization. Geneva: World Health Organization; 2021. Licence: CC BY‑NC‑SA 3.0 IGO. Available from: https://www.who.int/docs/default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf
Outlines global priorities and policy frameworks for digital health adoption, governance, and equity to support health system strengthening worldwide.
Observer-Independent Assessment of Psoriasis-Affected Area Using Machine Learning. Meienberger N, Anzengruber F, Amruthalingam L, et al. J Eur Acad Dermatol Venereol. 2020;34(6):1362-1368. doi:10.1111/jdv.16002
Presents a machine learning–based approach for objectively quantifying psoriasis-affected body surface area, minimizing interobserver variability in clinical assessment.
Smart Identification of Psoriasis by Images using Convolutional Neural Networks: A Case Study in China. Zhao S, Xie B, Li Y, et al. J Eur Acad Dermatol Venereol. 2020;34(3):518-524. doi:10.1111/jdv.15965
Evaluates a CNN-based image recognition system for psoriasis detection, demonstrating high diagnostic accuracy and potential for real-world clinical application.
Practice Guidelines for Teledermatology. McKoy K, Antoniotti NM, Armstrong A, et al. Telemed J E Health. 2016;22(12):981-990. doi:10.1089/tmj.2016.0137
Provides evidence-based recommendations for establishing and delivering teledermatology services, addressing clinical workflows, technical standards, and legal considerations.
Page last updated: January 2026