An Agentic AI Framework for Interpretive Inductive Analysis of Multimodal Clinical Interviews: Leveraging Multi-agent and Bio-Medical Knowledge Graph

Multimodal Clinical Communication Interpretation: Interpreting patient-provider communication across secure messaging, documentation, calls, and video interviews is essential for informed clinical decision-making and trial eligibility assessment.

Scalable Unstructured Clinical Data Analysis: Analyzing unstructured multimodal clinical data at scale is time-intensive and requires expert clinical insight, burdening healthcare professionals.

Multimodal Clinical Communication Interpretation: Interpreting patient-provider communication across secure messaging, documentation, calls, and video interviews is essential for informed clinical decision-making and trial eligibility assessment.

Scalable Unstructured Clinical Data Analysis: Analyzing unstructured multimodal clinical data at scale is time-intensive and requires expert clinical insight, burdening healthcare professionals.

Agentic Multi-Agent Clinical Analysis Framework: The paper proposes a modular multi-agent Agentic AI framework to augment and streamline qualitative analysis in healthcare settings.

LLM Knowledge-Graph Clinical Agents: The framework integrates reasoning-capable LLMs with domain-specific medical knowledge graphs to support specialized clinical analysis agents.

LangGraph-Orchestrated Shared-Memory Workflow: The agents are coordinated through a LangGraph-style orchestration layer with shared memory to provide context-aware, adaptive, and error-resilient workflows.

Transparent Automated Qualitative Analysis: The system automates labor-intensive qualitative tasks while embedding transparency and oversight mechanisms.

Agentic Multi-Agent Clinical Analysis Framework: The paper proposes a modular multi-agent Agentic AI framework to augment and streamline qualitative analysis in healthcare settings.

Reduced Clinician Workload:

Faster Patient Narrative Interpretation:

Expert-Aligned Interpretations: