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Related Experiment Video

Updated: Jan 22, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A Perspective on Software Intelligence for Autonomous Transformations in Biomedical Data and Knowledge.

Vivek Navale1

  • 1Center for Information Technology, National Institutes of Health Bethesda Maryland USA.

Learning Health Systems
|January 21, 2026
PubMed
Summary

Software intelligence and multi-agent systems can automate data-to-knowledge transformations, enhancing the learning health system (LHS) cycle. This approach improves data reuse and accelerates biomedical discoveries by enabling autonomous knowledge generation.

Keywords:
intelligent biomedical platformintelligent data agentsmulti‐agent systemsoftware intelligencesoftware learning

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence
  • Knowledge Management

Background:

  • Persistent knowledge is crucial for the learning health system (LHS) cycle, involving data-to-knowledge transformations.
  • Human efforts face challenges with large-scale, complex data, leading to underutilized datasets.
  • Specialized software agents offer a solution for learning tasks, goal discovery, and human interaction.

Purpose of the Study:

  • To examine the role of software intelligence in autonomous data and knowledge transformation.
  • To explore the application of multi-agent systems for various data and knowledge-mining tasks.
  • To propose a framework for an intelligent biomedical platform (IBP) leveraging these technologies.

Main Methods:

  • Conceptualized an Intelligent Biomedical Platform (IBP) with four layers: infrastructure, user interface, coordination system, and data/knowledge.
  • Integrated a network of multi-agent systems for clinical decision-making and knowledge mining.
  • Leveraged knowledge representation, ontologies, semantic web standards, and large language models for enhanced reasoning.

Main Results:

  • The proposed IBP framework integrates agents for autonomous data and knowledge transformation.
  • Intelligence arises from the interaction of infrastructure, user interface, coordination, and data/knowledge agents.
  • Agents can perform inferential steps and leverage semantic relationships for goal generation and improved accuracy.

Conclusions:

  • An IBP with multi-agent systems enhances autonomous data-to-knowledge transformation and data reuse.
  • Software intelligence in repositories boosts knowledge generation and utilization.
  • Incorporating reasoning capabilities into biomedical platforms efficiently propagates the LHS cycle for new discoveries.