DXAI leverages a specialized AI stack that is purpose-built for medical diagnostics, combining advanced architectures, domain-specific training, and scalable deployment. Unlike general-purpose large language models (LLMs), our platform focuses on accuracy, interpretability, and efficiency in healthcare-specific applications.
Key Components of the DXAI AI Stack:
Model Architectures:
Vision Transformers (ViTs) and CNNs for medical image analysis.
Fine-tuned LLMs (e.g., Llava-Med, Mistral) for text-based diagnostics and reasoning.
Domain-Specific Training:
Trained on curated datasets such as SinoCT (CT scans), ISIC (dermatological images), and MURA (radiographs).
Specialized tuning ensures higher performance in sensitivity, specificity, and precision compared to generalized models.
Real-Time Capabilities:
Scalable architecture for processing thousands of cases daily.
Decentralized integration using $DXAI tokens for seamless access.
Advantages of DXAI Over Commercial LLMs:
Precision: Domain-specific models outperform general LLMs in medical diagnostics by up to 20%.
Efficiency: Optimized for medical tasks, reducing computational overhead.
Interpretable Outputs: Provides heatmaps, overlays, and context-aware reasoning for clinicians.
Decentralization: Blockchain-backed transparency and incentivization.
In a nutshell, considering commercial LLMs, this is the difference between our implementations and commercial LLMs:
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