To design, implement, evaluate, and quantify a novel and adaptable Artificial Intelligence-empowered methodology aimed at supporting a dermatologist's workflow in assessing and diagnosing skin conditions, leveraging AI's deep image analytic power and reasoning. Skin presents diverse conditions that no single AI solution can comprehensively address, suggesting that mimicking a medical professional's diagnostic process and creating strategic AI interventions may enhance decision-making. We employ large language, transformer-based vision models for image analysis, sophisticated machine learning tools for guideline-based segmentation, and measuring tasks in our system. As no single technology is sufficient on its own for efficient use by dermatologists, we apply a sequential logic with agency to improve outcomes. Using natural language processing methods and incorporating human expert evaluation, our system achieved a weighted accuracy of 87% on the dataset used, demonstrating its reasoning and diagnostic capabilities. This study serves as a proof of concept for the application of AI in dermatology, highlighting its potential to enhance the patient journey for which we approximate the value of such interventions in healthcare using graph theory with an associated cost-optimization objective function.

The rise of Artificial Intelligence creates great promise in the field of medical discovery, diagnostics and patient management. However, the vast complexity of all medical domains require a more complex approach that combines machine learning algorithms, classifiers, segmentation algorithms and, lately, large language models. In this paper, we describe, implement and assess an Artificial Intelligence-empowered system and methodology aimed at assisting the diagnosis process of skin lesions and other skin conditions within the field of dermatology that aims to holistically address the diagnostic process in this domain. The workflow integrates large language, transformer-based vision models and sophisticated machine learning tools. This holistic approach achieves a nuanced interpretation of dermatological conditions that simulates and facilitates a dermatologist's workflow. We assess our proposed methodology through a thorough cross-model validation technique embedded in an evaluation pipeline that utilizes publicly available medical case studies of skin conditions and relevant images. To quantitatively score the system performance, advanced machine learning and natural language processing tools are employed which focus on similarity comparison and natural language inference. Additionally, we incorporate a human expert evaluation process based on a structured checklist to further validate our results. We implemented the proposed methodology in a system which achieved approximate (weighted) scores of 0.87 for both contextual understanding and diagnostic accuracy, demonstrating the efficacy of our approach in enhancing dermatological analysis. The proposed methodology is expected to prove useful in the development of next-generation tele-dermatology applications, enhancing remote consultation capabilities and access to care, especially in underserved areas.