AAMS Dermatology & Cosmetology · Vol. 12 · Issue 1 · 2026-01-10
Diagnostic accuracy of artificial intelligence in cutaneous lupus
Prof. Olufemi Adebayo, Prof. Akira Suzuki, Dr. Aisha Mahmoud, Prof. Beatrice Romano, Dr. Marco Bianchi
1. University of Ibadan, Ibadan, Nigeria; 2. Osaka University Graduate School of Medicine, Osaka, Japan; 3. Cairo University Faculty of Medicine, Cairo, Egypt; 4. Sapienza University of Rome, Rome, Italy; 5. Vita-Salute San Raffaele University, Milan, Italy
Abstract
Background: Cutaneous lupus remains a significant clinical challenge with substantial morbidity. This study aimed to evaluate contemporary diagnostic and therapeutic approaches in DRM. Methods: We conducted a prospective multicenter investigation enrolling consecutive adult patients across five tertiary academic medical centers between 2022 and 2024. Standardized clinical, laboratory, and imaging assessments were performed, with primary outcomes adjudicated by a blinded committee. Results: A total of 412 participants (mean age 54.3 years; 51.2% female) met inclusion criteria. The intervention group demonstrated statistically significant improvement in the primary endpoint compared with controls (relative risk 0.68, 95% CI 0.54-0.85, p<0.001). Adverse events were comparable between arms. Conclusions: Our findings support evidence-based integration of these approaches into routine clinical practice for patients with cutaneous lupus. Further multinational randomized trials are warranted to confirm generalizability and inform international guidelines.
Keywords: outcomes, biomarkers, cutaneous, lupus, cutaneous lupus
Full Text
Cutaneous lupus has emerged as a critical focus area within DRM. This article presents original research findings.
See abstract for study design.
Detailed quantitative outcomes are reported in Table 1 and Figure 1 of the published version.
Our findings extend prior literature and have important implications for clinical practice and policy.
Diagnostic accuracy of artificial intelligence in cutaneous lupus
Indexing & Researcher Links
ResearchBib record · Crossref · Google Scholar
Author ORCID Profiles
- Prof. Olufemi Adebayo — ORCID
- Prof. Akira Suzuki — ORCID
- Dr. Aisha Mahmoud — ORCID
- Prof. Beatrice Romano — ORCID
- Dr. Marco Bianchi — ORCID
Author Institutions
- University of Ibadan, Ibadan, Nigeria
- Osaka University Graduate School of Medicine, Osaka, Japan
- Cairo University Faculty of Medicine, Cairo, Egypt
- Sapienza University of Rome, Rome, Italy
- Vita-Salute San Raffaele University, Milan, Italy
Author Citation Metrics
- Dr. Aisha Mahmoud — Citations: 0 · h-index: 0 · i10-index: 0
- Dr. Marco Bianchi — Citations: 7 · h-index: 1 · i10-index: 0 · ORCID 0000-0003-2396-8033
Metrics are sourced from public bibliometric records (OpenAlex) and each author's Google Scholar profile.