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MetaCog-RAG: Metacognitive Memory Networks for Reliable Healthcare Assistants

Abstract

A novel framework combining metacognitive assessment capabilities with retrieval-augmented generation (RAG) for creating more reliable healthcare AI assistants. The system continuously evaluates its own knowledge limitations and uncertainty while building an evolving knowledge graph of medical information, enabling more trustworthy clinical decision support.

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Research Gap Analysis

Current systems lack integration between metacognitive capabilities and knowledge retrieval systems, leading to overconfident and potentially dangerous medical AI assistants

MetaCog-RAG: Metacognitive Memory Networks for Reliable Healthcare Assistants

Motivation

Large Language Models (LLMs) have shown remarkable performance on medical examinations but exhibit critical metacognitive deficiencies that pose risks in clinical settings. Current RAG systems focus primarily on factual retrieval but lack the ability to recognize knowledge limitations and uncertainty - crucial capabilities for healthcare applications. Additionally, existing approaches struggle to maintain the dynamic, interconnected nature of medical knowledge while ensuring reliability.

Proposed Approach

MetaCog-RAG introduces three key innovations:

1. Metacognitive Assessment Layer

  • Implements a specialized transformer architecture that maintains separate representations for knowledge content and confidence estimation
  • Trains on MetaMedQA-style datasets to learn appropriate uncertainty calibration
  • Uses contrastive learning to distinguish between known and unknown medical concepts

2. Dynamic Medical Knowledge Graph

  • Builds upon HippoRAG's Personalized PageRank algorithm but extends it with medical domain constraints
  • Incorporates temporal relationships between medical concepts
  • Maintains confidence scores for knowledge graph edges based on evidence strength

3. Hierarchical Memory Integration

  • Short-term working memory for immediate context
  • Long-term episodic memory for past cases and experiences
  • Semantic memory implemented as the medical knowledge graph
  • Meta-memory layer for tracking reliability of different memory components

Expected Outcomes

  • Improved ability to recognize and communicate uncertainty in medical reasoning
  • More reliable and transparent clinical decision support
  • Better handling of edge cases and rare conditions
  • Continuous learning while maintaining reliability guarantees
  • Quantifiable confidence metrics for medical recommendations

Potential Applications

  • Clinical decision support systems
  • Medical education and training
  • Research literature analysis
  • Patient triage and preliminary assessment
  • Drug interaction checking
  • Rare disease diagnosis support

Proposed Methodology

Develop a hierarchical system combining metacognitive assessment, dynamic medical knowledge graphs, and multi-level memory architecture with explicit uncertainty handling

Potential Impact

Could enable safer deployment of AI in healthcare settings by providing reliable self-assessment of capabilities and limitations, potentially reducing medical errors and improving patient care

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