Role of Artificial Intelligence in Pediatric Dermatology Diagnosis and Teledermatology
Keywords:
Bacillus cereus, FTIR, In vitro antibacterial activity, Solanum trilobatum, Silver nanoparticle, UV- spectroscopy.Abstract
Background: Artificial intelligence (AI) has emerged as a transformative technology in dermatology by improving diagnostic accuracy and expanding telemedicine services. Pediatric dermatology particularly benefits from AI-assisted teledermatology because early diagnosis and prompt management of skin diseases are essential to prevent complications and psychosocial distress. Aim: To evaluate the role of artificial intelligence in pediatric dermatology diagnosis and teledermatology at Nalanda Medical College and Hospital (NMCH). Materials and Methods: A hospital-based prospective observational study was conducted at NMCH from September 2025 to February 2026 among 280 pediatric patients presenting with dermatological complaints. Clinical diagnoses established by dermatologists were compared with AI-assisted teledermatology diagnoses. Diagnostic accuracy, sensitivity, specificity, concordance rate, consultation time, and patient satisfaction were assessed. Statistical analysis was performed using SPSS version 26.0. A p-value <0.05 was considered statistically significant. Results: Eczema (24.3%) was the most common diagnosis followed by fungal infections (18.6%) and acneiform disorders (14.3%). AI-assisted diagnosis demonstrated an overall diagnostic accuracy of 88.2%, sensitivity of 90.4%, and specificity of 85.7%. Mean consultation time was significantly reduced with AI-assisted teledermatology (18.6 ± 5.4 minutes) compared to conventional consultation (42.5 ± 8.2 minutes) (p<0.001). Diagnostic concordance between AI-assisted systems and dermatologist diagnosis was observed in 247 cases (88.2%). Conclusion: Artificial intelligence integrated with teledermatology demonstrated high diagnostic accuracy and significantly improved efficiency in pediatric dermatology services. AI-assisted teledermatology can serve as an effective adjunctive tool in resource-limited healthcare settings.
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Copyright (c) 2026 Ashutosh Kumar Mishra, Shivani Shekhar, Puja Kumari, Ramavatar Singh
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