Large-Scale Model Technical Architecture

HealthSync AI’s core technology relies on the development and optimization of large-scale models (Large Language Models, LLMs, and multimodal models). The key technical components are outlined below:

3.1 Data Collection and Preprocessing

• Data Sources: The platform aggregates anonymized global medical data, including medical records, imaging, lab reports, and treatment outcomes.

• Data Cleaning: Utilizes natural language processing (NLP) to process unstructured medical texts (e.g., physician notes, patient descriptions) and convert them into structured data.

• Privacy Protection: Employs differential privacy and federated learning to ensure data anonymization, complying with HIPAA and GDPR regulations.

3.2 Large-Scale Model Training

• Model Types: HealthSync AI uses multimodal large-scale models capable of processing text, imaging, and time-series data.

• Training Framework:

  • Pretraining: Conducted on vast medical literature, public datasets (e.g., PubMed, MIMIC-III), and synthetic data.

  • Domain Fine-Tuning: Tailored for specific medical scenarios (e.g., radiology, pathology) to optimize performance in case matching and diagnostic support.

  • Continuous Learning: Online learning mechanisms enable the model to incorporate new data in real-time, keeping knowledge up-to-date.

• Hardware Support: Leverages GPU/TPU clusters for efficient training, using distributed computing frameworks (e.g., PyTorch Distributed) to accelerate iterations.

3.3 Model Functionality Implementation

• Case Matching:

  • Employs embedding models to map patient data (symptoms, imaging, history) into high-dimensional vector spaces.

  • Uses cosine similarity or graph neural networks (GNNs) to compute similarities with a global case library, delivering matched results.

• Image Recognition:

  • Utilizes multimodal models combining convolutional neural networks (CNNs) and Transformers to analyze medical imaging (e.g., CT, MRI).

  • Enables lesion detection, classification (e.g., benign vs. malignant tumors), and anomaly annotation.

• Symptom Analysis:

  • Parses patient-input natural language descriptions via NLP modules to extract key symptoms.

  • Combines knowledge graphs to infer disease probabilities and provide diagnostic recommendations.

• Telemedicine Support:

  • Integrates speech recognition and generative models to support multilingual real-time conversations.

  • Generates patient-friendly medical reports and treatment recommendations using generative models.

3.4 Model Deployment and Optimization

• Inference Optimization: Applies quantization and pruning techniques to reduce inference latency, ensuring real-time responses.

• Edge Computing: Deploys lightweight models to edge devices (e.g., mobile devices) for offline symptom analysis in telemedicine scenarios.

• Scalability: Utilizes Kubernetes clusters and microservices architecture to ensure high availability and elastic scaling of model services.