How Artificial Intelligence Is Advancing Precision Medicine in Inflammatory Skin Diseases

A new review is putting a spotlight on how rapid advances in artificial intelligence (AI) are beginning to reshape how clinicians diagnose, monitor, and treat inflammatory skin diseases, offering new opportunities to move beyond symptom-based care toward precision medicine.¹

Throughout the review, published in the Journal of Investigative Dermatology, researchers outline the growing role of AI and machine learning (ML) in improving disease classification, identifying patient subtypes, and guiding targeted therapeutic strategies across a range of chronic dermatologic conditions.

Inflammatory skin diseases, spanning atopic dermatitis, psoriasis, hidradenitis suppurativa, rosacea, alopecia areata, and vitiligo, are driven by complex immune pathways and exhibit significant clinical variability between patients. The increasing availability of multimodal datasets spanning imaging, genomic, transcriptomic, and clinical data has enabled AI-based computational methods to extract clinically meaningful insights from previously unmanageable volumes of information.

Traditionally, dermatologic diagnosis has relied heavily on visual assessment. Recent developments in computer vision have expanded AI’s diagnostic capabilities beyond melanoma detection² to encompass a broader spectrum of inflammatory conditions. The review notes that ML models are now being trained to classify diseases such as acne, eczema, psoriasis, and vitiligo using clinical photography, dermatoscopic imaging, histologic slides, and electronic health record data.¹

“The primary focus of most of the AI-based studies are on AD and psoriasis, which is expected because they are 2 of the most common inflammatory skin diseases,” noted the researchers. “Significantly fewer high-quality studies exist for acne, rosacea, AA, vitiligo, and HS. The fewest studies focused on rheumatologic skin disease (eg, lupus, DM, morphea/scleroderma, vasculitis) and autoimmune blistering disorders.”

Importantly, AI is also helping clinicians move beyond diagnosis toward deeper understanding of disease heterogeneity, highlighted the researchers. Multimodal AI approaches integrate clinical features, environmental exposures, molecular data, and pathology findings to support phenotyping, prognostication, and personalized treatment selection.

This capability is particularly relevant for autoimmune conditions such as systemic lupus erythematosus and scleroderma, where patient responses to therapy often vary widely, highlighted the researchers. ML-driven clustering algorithms can identify molecular subtypes of disease based on gene expression patterns or treatment response trajectories, which may help inform targeted therapy selection and improve clinical outcomes.

The review also detailed how AI is being explored in therapeutic development. Computational models capable of analyzing protein–drug interaction networks may accelerate drug discovery or enable repurposing of existing therapies. For example, ML-based approaches using transcriptomic data have identified candidate treatments for both atopic dermatitis and psoriasis by linking enriched genetic pathways in lesional skin to potential pharmacologic targets.

Beyond research applications, AI is beginning to support real-world clinical workflows. Emerging tools such as AI-powered documentation systems and teledermatology platforms can assist with virtual consultations, image interpretation, and clinical decision support, potentially improving access to specialist care in underserved or remote populations.

Generative AI may also enhance patient education by translating complex dermatopathology reports into accessible language or supporting communication around chronic disease management. However, studies evaluating large language model–generated responses to patient questions have identified occasional inaccuracies, highlighting the need for ongoing validation before widespread implementation.

Despite these advances, the review underscored several persistent challenges. Many AI models remain trained on limited imaging datasets, raising concerns about generalizability, particularly among patients with skin of color. Additionally, algorithmic bias and inconsistent validation continue to pose barriers to equitable deployment of AI-based tools in clinical practice.

Additional technical hurdles include standardizing image quality, mitigating confounding artifacts, and integrating predictive algorithms into existing healthcare workflows. Concerns regarding data privacy, misinformation, and potential inaccuracies in AI-generated clinical recommendations further emphasize the importance of ethical oversight and multidisciplinary collaboration.

Looking ahead, researchers suggested that integrating AI-driven analytics with multimodal patient data, including genetic, environmental, and clinical information, may enable clinicians to personalize treatment for their patients. Such approaches could reduce reliance on trial-and-error treatment selection and support earlier intervention strategies for inflammatory skin diseases.

References

1. Tang AS, Wei ML, Haemel A, et al. Artificial intelligence–enabled precision medicine for inflammatory skin diseases. J Invest Dermatol. Published online Februarry 13, 2026. doi:10.1016/j.jid.2025.10.596

2. Muacevic A, Adlerr JRR. The role of artificial intelligence in the diagnosis of melanoma. Cureus. 2024;16(9):e69818. doi:10.7759/cureus.69818