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Medicina
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Recent Advances in Diagnosis and Therapy of Inflammatory Skin Diseases
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Recent Advances in Diagnosis and Therapy of Inflammatory Skin Diseases

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Dermatology".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1029

Special Issue Editor

Dr. Neil Kunal Jairath

E-Mail Website
Guest Editor
Department of Dermatology, Indiana University School of Medicine, Indianapolis, IN, USA
Interests: inflammatory skin diseases; teledermatology; AI in dermatology; Mohs surgery; dermatologic complexity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Inflammatory skin diseases represent a significant global health burden, affecting millions of patients worldwide and substantially impacting quality of life. These immune-mediated conditions, including psoriasis, atopic dermatitis, hidradenitis suppurativa, vitiligo, and other dermatoses, have historically posed diagnostic and therapeutic challenges. However, recent years have witnessed transformative advances that are reshaping clinical practice and patient outcomes.

Diagnostic innovation has been accelerated by the integration of artificial intelligence and machine learning applications, enabling more accurate disease classification, severity assessment, and outcome prediction. Novel imaging modalities, biomarker discovery, and digital health technologies are revolutionizing clinical evaluation and monitoring strategies. Concurrently, the therapeutic landscape has expanded dramatically with targeted biologic agents, small molecule inhibitors (including JAK inhibitors), and emerging gene therapies, offering unprecedented treatment options for patients with moderate-to-severe disease.

We are pleased to invite you to contribute to this Special Issue, which aims to showcase cutting-edge research across the translational spectrum of inflammatory skin diseases.

This Special Issue aims to provide a comprehensive overview of recent advances in the diagnosis and treatment of inflammatory skin diseases, aligning with Medicina's scope of publishing high-quality clinical and translational research. By bringing together innovative diagnostic tools, breakthrough therapeutic strategies, and mechanistic insights, this collection will serve as a valuable resource for clinicians, researchers, and healthcare professionals seeking to optimize patient care. The integration of artificial intelligence in dermatology represents a particularly timely focus area, reflecting the rapid technological transformation of our field.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Novel diagnostic technologies and artificial intelligence applications in inflammatory skin diseases;
  • Biomarker discovery and validation for disease diagnosis, prognosis, and treatment response;
  • Advances in biologic therapies for psoriasis, atopic dermatitis, and other inflammatory dermatoses;
  • JAK inhibitors and small-molecule therapeutics in inflammatory skin conditions;
  • Precision medicine approaches and personalized treatment strategies;
  • Digital health technologies and telemedicine in dermatology;
  • Comparative effectiveness research and real-world evidence studies;
  • Mechanisms of disease pathogenesis and therapeutic targets;
  • Treatment of refractory or difficult-to-treat inflammatory skin diseases;
  • Health outcomes research and quality of life assessments;
  • Emerging therapies, including gene therapy and microbiome-based interventions;
  • Pediatric inflammatory skin diseases: diagnosis and management;
  • Combination therapy strategies and treatment optimization.

We look forward to receiving your contributions.

Dr. Neil Kunal Jairath
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Medicina is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • inflammatory skin diseases
  • artificial intelligence
  • dermatology
  • biologic therapy
  • psoriasis
  • atopic dermatitis
  • precision medicine
  • JAK inhibitors
  • digital health
  • immune-mediated dermatoses

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

16 pages, 339 KB  
Article
IL12B rs3213094 as a Predictor of Early Response to Biologic Therapy in Psoriasis: A Real-World Study in a Romanian Cohort
by Alessandra-Madalina Matei-Man, Ildiko-Orsolya Gaal, Andreea Catana, Stefan Vesa, Simona Senila, Elisabeta Candrea, Meda Orasan, Alexandra Puskas, Ana Calina Man and Teodora Mocan
Medicina 2026, 62(6), 1041; https://doi.org/10.3390/medicina62061041 - 28 May 2026
Viewed by 62
Abstract
Background and Objectives: Psoriasis is a chronic immune-mediated inflammatory disease characterized by heterogeneous clinical presentation and variable response to biologic therapy. Genetic variation within the IL-23/Th17 inflammatory pathway may influence treatment outcomes. This study evaluated the association between IL12B rs3213094 and IL23R [...] Read more.
Background and Objectives: Psoriasis is a chronic immune-mediated inflammatory disease characterized by heterogeneous clinical presentation and variable response to biologic therapy. Genetic variation within the IL-23/Th17 inflammatory pathway may influence treatment outcomes. This study evaluated the association between IL12B rs3213094 and IL23R rs11209026 single-nucleotide polymorphisms (SNPs) and response to biologic therapy in patients with moderate-to-severe psoriasis. Materials and Methods: We conducted a multicenter observational study including 92 Romanian patients with moderate-to-severe psoriasis vulgaris receiving their first biologic therapy (anti-TNF, anti-IL-17, or anti-IL-23 monoclonal antibodies). Clinical response was assessed using the Psoriasis Area and Severity Index (PASI) at baseline and weeks 12, 24, 36, and 48. Early response was defined as achieving PASI75 at week 12. Patient-reported disease impact was assessed using the Dermatology Life Quality Index (DLQI) at the same time points. Genotyping of IL12B rs3213094 and IL23R rs11209026 was performed using TaqMan assays. Longitudinal PASI dynamics were analyzed using repeated-measures ANOVA, while multivariable logistic regression was used to identify independent predictors of PASI75 at week 12. Results: A significant reduction in PASI scores over time was observed (p < 0.001). The IL12B rs3213094 genotype was associated with differences in early response kinetics, with T-allele carriers showing significantly greater PASI improvement at week 12 compared with CC homozygotes (90.0% vs. 65.7%, p = 0.003). This effect was limited to early treatment and attenuated at later time points. In multivariable analysis, the IL12B rs3213094 CT + TT genotype was independently associated with PASI75 achievement at week 12 (OR = 4.285, 95% CI 1.500–12.239, p = 0.007). Treatment with anti-IL-17 agents was also an independent predictor of early response (OR = 3.946, 95% CI 1.416–10.998, p = 0.009). No significant association was observed between IL23R rs11209026 and treatment response. DLQI scores improved significantly over time (p < 0.001), without genotype-dependent differences. Conclusions: IL12B rs3213094 SNP is significantly associated with early biologic treatment response in psoriasis, supporting its potential role as a pharmacogenetic biomarker of treatment responsiveness. These findings may inform the integration of genetic markers into personalized therapeutic strategies, particularly in underrepresented populations such as those from Eastern Europe. Further studies in larger cohorts are warranted to validate these results. Full article
(This article belongs to the Special Issue Recent Advances in Diagnosis and Therapy of Inflammatory Skin Diseases)
14 pages, 871 KB  
Article
Validation of a Dermatology-Focused Multimodal Image-and-Data Assistant in Diagnosis and Management of Common Dermatologic Conditions
by Joshua Mijares, Emma J. Bisch, Eanna DeGuzman, Kanika Garg, David Pontes, Neil K. Jairath, Vignesh Ramachandran, George Jeha, Andjela Nemcevic and Syril Keena T. Que
Medicina 2026, 62(4), 715; https://doi.org/10.3390/medicina62040715 - 9 Apr 2026
Viewed by 625
Abstract
Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic [...] Read more.
Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic presentations underserved. This study evaluated the diagnostic accuracy and management plan quality of Dermflow (Prava Medical, Delaware, USA), a proprietary dermatology-focused Multimodal Image-and-Data Assistant (MIDA) that autonomously gathers dermatology-specific history, integrates data with patient-submitted images, and outputs structured differential diagnoses and management summaries. Materials and Methods: Two AI systems, Dermflow and Claude Sonnet 4 (Claude, a leading vision–language model), analyzed 87 clinical images from the Skin Condition Image Network and Diverse Dermatology Images databases, representing 10 inflammatory dermatoses and 9 neoplastic conditions stratified across Fitzpatrick Skin Tone (FST) categories (I–II, III–IV, V–VI). For the diagnostic comparison, Dermflow received images and autonomously gathered clinical history, while Claude received identical images without history. For the management plan comparison, both systems received the correct diagnosis and the clinical histories gathered by Dermflow. The primary outcome was diagnostic accuracy. The secondary outcome was management plan quality, assessed by two blinded dermatologists across eight clinical dimensions using 5-point Likert scales. Chi-square tests compared diagnostic accuracy between models; t-tests and ANOVA compared management quality scores. Results: Dermflow achieved markedly superior diagnostic accuracy compared to Claude (86.2% vs. 24.1%, p < 0.001). Both models maintained consistent diagnostic performance across FST categories without significant within-model differences (Dermflow p = 0.924; Claude p = 0.828). Management plan quality showed no significant overall differences between models. However, composite management quality scores declined significantly for darker skin tones across both systems: Dermflow scored 4.20 (FST I–II), 3.99 (FST III–IV), and 3.47 (FST V–VI); Claude scored 4.35, 3.97, and 3.44, respectively (p < 0.001 for most pairwise FST comparisons within each model). Conclusions: Multimodal AI integrating targeted history with image analysis achieves substantially higher diagnostic accuracy than image-only approaches across both inflammatory and neoplastic dermatologic conditions. Autonomous history gathering addresses fundamental limitations of morphology-only classifiers and enables scalable, patient-facing triage across the full spectrum of dermatologic disease. However, both models demonstrated reduced management plan quality for darker skin tones despite receiving the correct diagnosis, suggesting persistent training data limitations that require targeted bias-mitigation strategies beyond domain-specific instruction. Full article
(This article belongs to the Special Issue Recent Advances in Diagnosis and Therapy of Inflammatory Skin Diseases)
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