Architecting Agentic AI Pipelines for Clinical Precision & Industrial ROI

Aim

Workshop Objectives

Workshop Structure

Day 1: The Diagnostic Engine & Vision Systems

• Transitioning from Classical CNNs to Vision Transformers (ViTs) in clinical settings.
• The “Black Box” Problem: Importance of Saliency Maps and Explainable AI (XAI) for radiologists.

• Hands-on Laboratory:
  • Tool: MONAI (Medical Open Network for AI)
  • Exercise: Fine-tuning a pre-trained Med-ViT model for multi-class classification of medical imagery (e.g., Chest X-rays or Histopathology slides).
  • Practical Outcome: Visualizing attention weights to validate clinical decision markers.

Day 2: Data Sovereignty & Synthetic Patient Modeling

• The “Data Silo” Paradox: Balancing HIPAA/GDPR compliance with the need for large-scale training sets.
• Digital Twins: Using Generative AI to simulate disease progression and clinical trials.

• Hands-on Laboratory:
  • Tool: Gretel.ai / YData Fabric
  • Exercise: Generating privacy-preserving Synthetic Electronic Health Records (EHR) from a seed dataset.
  • Practical Outcome: Creating a statistically accurate, shareable dataset that bypasses traditional IRB/Ethics Committee bottlenecks.

Day 3: Clinical Deployment & Agentic Assistance

• Agentic AI: Building autonomous agents that move from “summarizing notes” to “suggesting clinical pathways.”
• Interoperability: Integrating AI outputs into legacy hospital workflows using HL7 FHIR standards.

• Hands-on Laboratory:
  • Tool: LangChain + Streamlit
  • Exercise: Developing a “Clinical Co-Pilot” dashboard that parses unstructured clinician notes and maps them to ICD-11 diagnostic codes.
  • Practical Outcome: Deploying a functional web-based interface that demonstrates real-world ROI to healthcare stakeholders.

Who Should Enrol?

Important Dates

Workshop Outcomes

Fee Structure

What You’ll Gain