Developing “Skintelix”: An Intelligent Virtual Assistant for Supporting Pharmacists’ Decision-Making in the Preliminary Assessment and Management of Common Dermatological Conditions
Remal Abduaziz Asaad1, Nouran Abbas2, Hadeel Salim3
1Remal Abdulaziz Asaad, Professor, Department of Medicinal Chemistry and Quality Control, Faculty of Pharmacy, University of Tishreen, Lattakia, Syria.
2Nouran Abbas, Student, Department of Medicinal Chemistry, Tishreen University, Lattakia, Syria.
3Hadeel Salim, Student, Department of Medicinal Chemistry, Tishreen University, Faculty of Pharmacy, University of Tishreen, Lattakia, Syria.
Manuscript received on 10 September 2025 | Revised Manuscript received on 24 September 2025 | Manuscript Accepted on 15 December 2025 | Manuscript published on 30 December 2025 | PP: 15-21 | Volume-6 Issue-1, December 2025 | Retrieval Number: 100.1/ijapsr.F409505061025 | DOI: 10.54105/ijapsr.F4095.06011225
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This project introduces “Skintelix,” a novel, multimodal artificial intelligence (AI) assistant designed to augment pharmacists’ decision-making in the preliminary assessment and management of common skin conditions. Leveraging advanced generative AI models, specifically GPT-4V, the system integrates textual symptom analysis with clinical image processing to provide accurate differential diagnoses and evidence-based treatment recommendations. The development of Skintelix followed a three-tiered architectural model, comprising a user-centric conversational interface, a robust AI core engine, and a scalable pharmacy integration layer. The model was trained on a multi-source dataset, combining established global dermatology image repositories (such as HAM10000, DDI, and SCIN) with real-world clinical data from community pharmacies and authoritative medical references. This hybrid approach was deliberately employed to mitigate common algorithmic biases and enhance clinical relevance. The system’s ability to process both images and text, its adoption of a conversational format, and its focus on locally relevant clinical contexts represent significant contributions to the field. Skintelix offers a comprehensive theoretical framework to enhance diagnostic accuracy, minimise unnecessary specialist referrals, and enable pharmacists to serve as a more effective first-line defence in dermatological care. While a full-scale clinical validation is a crucial next step, the preliminary design demonstrates high feasibility and considerable promise for sustainable deployment in challenging environments.
Keywords: Generative AI, Artificial Intelligence (AI), Community Pharmacy, Digital Health, Dermatology, Multimodal AI, Pharmacist, Skin Conditions.
Scope of the Article: Pharmacy Practice