Dermasense-AI: An Intelligent Skin Analysis System Driven by Computer Vision and Large Language Models

Dermasense-AI is an open-source intelligent skin analysis system that combines computer vision and large language models. It can detect, classify, and interpret skin conditions from images, providing accurate insights, automated reports, and scalable AI workflows. Its core innovation lies in its multimodal architecture, which integrates visual recognition and language interpretation capabilities. Its applications cover clinical medicine, medical education, and scientific research, offering a new technical solution for skin health services.

Artificial intelligence is rapidly expanding its applications in the healthcare field. Dermatology, with the visual characteristics of skin lesions, is naturally suitable for computer vision-assisted diagnosis. As a typical representative of this trend, the Dermasense-AI project combines computer vision and large language models to provide a new technical solution for the detection, classification, and interpretation of skin diseases.

Dermasense-AI adopts a multimodal architecture:

1. The visual module is based on convolutional neural networks and vision Transformers, trained on large-scale skin image datasets. It can recognize various skin conditions, enhance robustness through data augmentation and domain adaptation, and support highlighting key areas via attention mechanisms.
2. The large language model module converts visual findings into structured diagnostic reports, providing condition descriptions, etiological analysis, and recommendations.
3. The modular pipeline design ensures system scalability to adapt to different deployment environments.

The system's core functions include:

1. Automatic detection and classification (recognize acne, eczema, and other skin conditions, and provide confidence scores)
2. Intelligent report generation (standard medical format, including patient information, analysis results, diagnostic opinions, and recommendations)
3. Interpretability analysis (visualize the basis for model decisions)
4. Batch processing capability (support automated analysis of large-scale image datasets to accelerate research work)

Clinical field: As an assistant to dermatologists, it aids initial screening, reference for difficult cases, and remote diagnosis support. Medical education: Provides case resources to help students learn visual features and diagnostic reasoning. Research field: Automated analysis accelerates epidemiological studies, drug trials, and new disease identification.

Challenges in skin image analysis:

1. Impact of skin tone, lighting, and shooting devices: Improve generalization through large-scale diverse data training and domain adaptation techniques.
2. Class imbalance: Mitigate via data resampling, loss function adjustment, and transfer learning.
3. Balance between interpretability and accuracy: Use attention mechanisms and post-processing interpretation techniques to provide interpretable outputs while maintaining accuracy.

As an open-source project, Dermasense-AI supports community contributions, and its modular architecture facilitates the expansion of new functions. Future directions:

• Short-term: Expand disease types, optimize mobile performance, and add multilingual support.
• Mid-term: Integrate electronic health records, real-time video analysis, and personalized models.
• Long-term: Build an end-to-end intelligent skin health platform, integrating medical history, genetic data, and other information to provide precise services.

Dermasense-AI represents an important direction of AI in the medical field, combining computer vision and large language models to build an accurate and interpretable intelligent system. The open-source model promotes open technical collaboration, paving the way for the democratization and popularization of medical AI. We look forward to more innovations that make AI technology benefit more people.