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Related Experiment Video

Updated: Sep 12, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Development and Update of CDSS-AI Integration Framework for Introducing Medical AI into Clinical Practice.

Masahiko Nakatsui1,2, Yasushi Hirano1,2, Koichi Kashibe2

  • 1Graduate School of Medicine, Yamaguchi University.

Studies in Health Technology and Informatics
|August 8, 2025
PubMed
Summary

We created a new framework connecting medical artificial intelligence (AI) with clinical decision support systems (CDSS) and data warehouses (DWH). This enhances AI implementation in healthcare, improving reproducibility and sustainability for clinical practice.

Keywords:
Clinical Decision Support System (CDSS)Cross-platform technologyMedical AIOpen-source softwareVirtualization

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Implementing artificial intelligence (AI) in clinical practice faces challenges in reproducibility, scalability, and sustainability.
  • Integrating AI tools with existing healthcare infrastructure like Clinical Decision Support Systems (CDSS) and Data Warehouses (DWH) is complex.

Purpose of the Study:

  • To develop an integrative framework connecting medical AI, CDSS, and DWH.
  • To enhance the reproducibility, scalability, and sustainability of AI implementations in clinical settings.
  • To address system challenges and optimize efficiency for AI integration.

Main Methods:

  • Developed an integrative framework for connecting medical AI, CDSS, and DWH.
  • Conducted operational testing of the framework starting August 2022.
  • Utilized an AI system designed to predict culprit drugs for adverse effects during testing.

Main Results:

  • The developed framework successfully integrated medical AI with CDSS and DWH.
  • Operational testing demonstrated the framework's ability to address system challenges.
  • The system was optimized for efficiency in predicting adverse drug effects.

Conclusions:

  • The integrative framework provides a robust solution for implementing AI in clinical practice.
  • The approach enhances the sustainability and scalability of medical AI.
  • Future work will focus on integrating additional medical AIs for wider clinical applications.