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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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Advancing Patient-AI Conversations with Recursive Learning Memory Using Relational Frame Theory.

Rishu Goyal1, Yifei Dong1, Kun Yu1

  • 1University of Technology Sydney, Australia.

Studies in Health Technology and Informatics
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel AI framework using Relational Frame Theory (RFT) to enhance Artificial General Intelligence (AGI) in bedside care. The RFT-based approach improves patient data organization and retrieval for more nuanced, human-like AI interactions.

Keywords:
Bedside AIKnowledge GraphPatient InteractionsRelational Frame Theory

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Healthcare Technology

Background:

  • Integrating human psychological principles is crucial for advancing Artificial General Intelligence (AGI) in clinical settings.
  • Current AI systems require enhanced cognitive abilities like reasoning and context awareness for effective bedside care.
  • Patient data management needs to evolve beyond traditional methods to support relational learning.

Purpose of the Study:

  • To propose and evaluate an AI framework for bedside care that leverages human psychological principles.
  • To enhance AGI's cognitive abilities, including reasoning, problem-solving, and spatial awareness.
  • To improve AI's capacity for continuous learning and relational recall of patient information.

Main Methods:

  • Developed an AI framework grounded in Relational Frame Theory (RFT).
  • Organized patient data using RFT to mirror human relational patterns.
  • Implemented a dynamic knowledge graph for iterative and context-sensitive information retrieval.
  • Compared the framework against traditional Retrieval Augmented Generation (RAG).

Main Results:

  • The RFT-based AI framework demonstrated enhanced patient interactions.
  • Achieved deeper, more personalized engagement compared to traditional RAG.
  • Showcased superior emulation of nuanced, human-like understanding in AI systems.
  • Validated the framework's effectiveness in organizing and retrieving patient data relationally.

Conclusions:

  • The proposed RFT-based AI framework significantly advances AGI in bedside care.
  • This approach offers a more human-like and contextually aware AI for healthcare.
  • Future AI in healthcare can benefit from integrating psychological principles for improved patient outcomes.