Enterprise AI Analysis
DermETAS-SNA LLM: A Dermatology Focused Evolutionary Transformer Architecture Search with StackNet Augmented LLM Assistant
This paper introduces DermETAS-SNA LLM Assistant, an AI system that combines Dermatology-focused Evolutionary Transformer Architecture Search (ETAS) with a StackNet Augmented Large Language Model (LLM). The assistant dynamically learns skin-disease classifiers and provides medically informed descriptions. Key contributions include: an ETAS framework on SKINCON dataset for optimizing Vision Transformers (ViT) for dermatological feature representation, fine-tuned binary classifiers for 23 skin disease categories, a StackNet architecture for predictive robustness, a RAG pipeline (DERM-RAG) using Google Gemini 2.5 Pro LLM for personalized diagnostic descriptions, extensive experimental evaluations demonstrating superior F1-score (56.30% vs SkinGPT-4's 48.51%), and a domain-expert evaluation showing 92% agreement rate. A proof-of-concept prototype integrates these components for practical clinical and educational applications.
Revolutionizing Dermatology Diagnostics with AI
The DermETAS-SNA LLM Assistant offers a significant leap forward in AI-driven dermatological diagnosis, addressing critical limitations of existing systems. By integrating advanced computer vision with contextually aware LLMs, it enhances diagnostic precision, especially for rare conditions, and provides explainable, clinically grounded insights. This technology promises to reduce diagnostic delays, improve patient understanding, and support healthcare professionals in underserved communities, ultimately transforming patient care and medical education in dermatology.
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F1-Score Increase vs. SkinGPT-4
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Domain Expert Agreement Rate
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Skin Disease Categories Covered
Deep Analysis & Enterprise Applications
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Evolutionary Architecture Search
Explores the ETAS framework used to optimize Vision Transformer (ViT) architectures for dermatological feature representation, leveraging genetic algorithms for superior model performance on the SKINCON dataset.
0.716
Highest average F1-score achieved by the 12-layer transformer in ETAS.
Enterprise Process Flow
SKINCON Dataset
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Preprocessing
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Population Initialization
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Fitness Evaluation
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Parent Selection
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Crossover & Mutation
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Fitness Evaluation (offspring)
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Environment Selection
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SKINCON Model
StackNet Architecture
Details the StackNet, a modular stacking ensemble framework that integrates multiple fine-tuned, class-specific binary ViT classifiers to enhance predictive robustness and mitigate class imbalance issues.
| Metric | StackNet | SkinGPT-4 |
|---|---|---|
| Accuracy (%) | 59.89 | 52.92 |
| Precision (%) | 59.25 | 54.57 |
| Recall (%) | 55.29 | 46.83 |
| F1 Score (%) | 56.30 | 48.51 |
| MCC | 0.57 | 0.50 |
Key Benefits of StackNet Architecture:
- Superior Recall and F1-score: StackNet's prominent improvement is in Recall (55.29% vs. 46.83%), representing an increase by approximately 18.06%. This indicates that our model is better at identifying true positives and reducing false negatives, critical in medical diagnostics where missing a disease (e.g., melanoma) can have severe consequences. The harmonized F1-score increased by approximately 16.06%, improving from 48.51% to 56.30%, indicating a more robust overall performance.
- Enhanced Classification Quality: The higher Matthews Correlation Coefficient (MCC) of 0.57 compared to 0.50 signifies a better overall quality of binary classifications, especially important given the imbalanced nature of the DermNet dataset.
- Architectural Advantages: The performance gap can be attributed to the dual-level design of StackNet. SkinGPT-4 uses a single model for all 23 classes, which can struggle with inter-class similarities and data imbalance. In contrast, StackNet's first-level binary classifiers specialize in individual conditions, learning highly discriminative features. A second-level meta-classifier then synthesizes these expert outputs with deep features and statistical context, yielding more nuanced and accurate predictions. This design is particularly effective for rare or visually similar conditions that are often misclassified by monolithic models.
StackNet's Impact on Melanoma Detection
Despite not being among the seven classes chosen for the in-depth domain expert evaluation, Melanoma achieved the highest F1 score of 74.16% within the StackNet framework when using full unfreezing with 10-fold cross-validation. This demonstrates the critical effectiveness of our dual-level approach for identifying highly critical diagnoses, highlighting StackNet's ability to provide robust performance for individual disease categories, which is paramount in life-threatening conditions like melanoma.
Retrieval-Augmented Generation (RAG)
Explains the DERM-RAG pipeline, which leverages Google Gemini 2.5 Pro LLM and a repository of verified dermatological materials to generate personalized, contextually informed diagnostic descriptions and explanations for patients.
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Overall agreement rate from medical professionals on RAG-generated responses.
Addressing Hallucinations with DERM-RAG
A significant limitation of previous multimodal LLMs like SkinGPT-4 was the propensity for hallucinations, leading to a substantial 37.8% strong disagreement rate from domain experts. In stark contrast, our DERM-RAG module, by grounding responses in a curated medical corpus and leveraging Gemini 2.5 Pro's capabilities, achieved an impressive 92% agreement rate. This demonstrates how RAG effectively mitigates the hallucination problem, providing accurate and trustworthy diagnostic explanations that are clinically reliable and contextually relevant, a critical advancement for patient safety and doctor-patient communication.
Projected Efficiency Gains with DermETAS-SNA LLM
Estimate the potential annual cost savings and reclaimed work hours your enterprise could achieve by integrating our AI-powered dermatological assistant. Adjust the parameters below to see a customized projection.
Annual Cost Savings
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Annual Hours Reclaimed
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DermETAS-SNA LLM Implementation Roadmap
Our phased approach ensures a smooth integration and maximizes the impact of the DermETAS-SNA LLM Assistant within your clinical or educational setting.
Phase 1: Discovery & Customization
Initial consultations to understand specific institutional needs, data privacy requirements, and integration points. Begin data preparation and custom model fine-tuning with institutional data (if applicable).
Phase 2: Model Deployment & Integration
Deployment of the DermETAS-SNA framework into your existing infrastructure. API integration with EHR systems and development of custom UI elements for seamless workflow. Pilot testing with a small group of clinicians.
Phase 3: Validation & Training
Rigorous internal validation of AI outputs with your medical staff. Comprehensive training programs for clinicians and support staff to ensure optimal utilization and understanding of the AI assistant's capabilities and limitations.
Phase 4: Scalable Rollout & Monitoring
Full-scale deployment across departments/institutions. Continuous monitoring of model performance, user feedback, and iterative improvements to adapt to evolving clinical practices and data. Long-term support and maintenance.
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