Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology

TL;DR

Dermacen Analytica tackles the need for robust AI support in dermatology by fusing multimodal large-language models with vision-based lesion analysis and principled evaluation. It introduces a cross-model collaboration framework with rule-based guidance to mitigate hallucinations and enhance diagnostic reasoning. An evaluation pipeline across 72 images using textual similarity metrics, NLI, and expert review yields high contextual and diagnostic capability (~0.86–0.87), supporting its potential for tele-dermatology deployment. The work demonstrates promise for improving remote dermatologic care in underserved regions while acknowledging ethical, privacy, and validation considerations and outlining directions for continuous learning and clinical collaboration.

Abstract

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.

Figures (8)

• Figure 1: Dermacen Analytica, proposed AI-empowered dermatology workflow
• Figure 2: Reducing the knowledge space
• Figure 3: Creating a premise and a hypothesis
• Figure 4: Evaluation process
• Figure 5: Skin lesion Use-Case