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Intelligent Conversational Agents in Patient Self-Management: A Systematic Survey Using Multi Data Sources.

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Intelligent conversation agents (ICAs) support patient self-management. This review identifies research gaps and focused areas in ICA innovation for better healthcare applications.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Human-Computer Interaction

Background:

  • Intelligent conversation agents (ICAs) are increasingly utilized for patient self-management and support.
  • The integration of ICAs into healthcare systems offers potential for improved patient engagement and outcomes.
  • Understanding the current landscape of ICA research and innovation is crucial for future development.

Purpose of the Study:

  • To systematically review research and innovation in Intelligent Conversation Agents (ICAs) for patient self-management.
  • To analyze bibliometric and patent data from academic and industrial institutions.
  • To identify disease types, patient populations, and delivery approaches for ICAs in self-management.

Main Methods:

  • Bibliometric analysis of academic publications.
  • Patent analysis of industrial innovations.
  • Systematic literature review of ICA applications in patient self-management.

Main Results:

  • Identified prevalent disease categories and patient demographics targeted by ICAs.
  • Cataloged diverse ICA delivery approaches for patient self-management.
  • Highlighted key research and innovation trends in the field.
  • Pinpointed specific gaps in current ICA productivity and focus areas.

Conclusions:

  • ICAs show significant promise for enhancing patient self-management across various conditions.
  • Further research is needed to address identified gaps in ICA development and application.
  • Targeted innovation efforts can optimize ICA utility in healthcare settings.