Expanding personalized, data-driven dermatology: leveraging digital health technology and machine learning to improve patient outcomes
Open accessAbstract
The current revolution of digital health technology and machine learning offers enormous potential to improve patient care. Nevertheless, it is essential to recognize that dermatology requires an approach different from those of other specialties. For many dermatological conditions, there is a lack of standardized methodology for quantitatively tracking disease progression and treatment response (clinimetrics). Furthermore, dermatological diseases impact patients in complex ways, some of which can be measured only through patient reports (psychometrics). New tools using digital health technology (e.g., smartphone applications, wearable devices) can aid in capturing both clinimetric and psychometric variables over time. With these data, machine learning can inform efforts to improve health care by, for example, the identification of high-risk patient groups, optimization of treatment strategies, and prediction of disease outcomes. We use the term personalized, data-driven dermatology to refer to the use of comprehensive data to inform individual patient care and improve patient outcomes. In this paper, we provide a framework that includes data from multiple sources, leverages digital health technology, and uses machine learning. Although this framework is applicable broadly to dermatological conditions, we use the example of a serious inflammatory skin condition, chronic cutaneous graft-versus-host disease, to illustrate personalized, data-driven dermatology.
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AI tools can be utilized to match patients with optimal therapies on the basis of individual disease pathways and subphenotypes.
“By moving beyond images and integrating data such as genetic profiles, transcriptomics, exposomics, clinical information, and patient outcomes, AI tools can be utilized to match patients with optimal therapies on the basis of individual disease pathways and subphenotypes (Patrick et al, 2018a; Wongvibulsin et al, 2022).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Wongvibulsin et al, 2022
The Abstract and “Personalized, data-driven dermatology” section describe using machine learning and data from multiple sources to optimize treatment strategies and inform individual care. They do not specifically say that AI matches patients to optimal therapies on the basis of individual disease pathways or subphenotypes. Page numbers are unavailable in our copy. (LLM, conf=0.84)
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Current State of Dermatology Mobile Applications With Artificial Intelligence Features
Wongvibulsin, S., Yan, M. J., Pahalyants, V., Murphy, W., Daneshjou, R., & Rotemberg, V. (2024) · JAMA Dermatology · doi:10.1001/jamadermatol.2024.0468 · Cited by 69 · see citing works
Self-citationArtificial intelligence–enabled precision medicine for inflammatory skin diseases
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- Authors
- Wongvibulsin, S., Frech, T. M., Chren, M.-M., & Tkaczyk, E. R.
- Affiliations
- Johns Hopkins University (US)
- United States Department of Veterans Affairs (US)
- Vanderbilt University Medical Center (US)
- Corresponding author
- Tkaczyk, Eric R.
- Venue
- JID Innovations
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 2
- Issue
- 3
- Pages
- 100105
- ISSN
- 2667-0267 (ISSN-L)
- PMC
- PMC9026581
- OpenAlex
- W4210698384
- Cited by
- 29
- References
- 54
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 10 · 2023: 6 · 2022: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- JID Innovations (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- Funders
- Clinical Science Research and Development
- National Institutes of Health (F30HL142131, K23AR067889, R01AR073001, 5T32GM007309)
- US Department of Veterans Affairs
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Dermatology › Dermatology and Skin Diseases
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- data-driven medicine, digital health technologies, machine learning, clinimetrics, psychometrics, chronic cutaneous graft-versus-host disease, inflammatory skin diseases, mobile application, wearable devices, disease progression monitoring, treatment response, high-risk patient identification, disease outcome prediction, multi-source data fusion
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