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Related Experiment Video

Updated: Jul 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A knowledge-anchored integrative image search and retrieval system.

Selnur Erdal¹, Umit V Catalyurek, Philip R O Payne

¹Information Warehouse, The Ohio State University Medical Center, 640 Ackerman Road, P.O. Box 183111, Columbus, OH 43218, USA. selnur.erdal@osumc.edu

Journal of Digital Imaging

|November 28, 2007

Summary

This study introduces a computational system for integrating diverse clinical data, including structured data, text, and images, to advance cancer research. The system uses ontology-anchored queries and Grid services for multi-site collaboration and ensures data security.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Biomedical Informatics
Computational Biology
Health Data Science

Background:

Clinical data is stored in diverse formats: structured, narrative text, and images.
Integrating these disparate data types is crucial for secondary research use.
Existing systems often lack robust methods for multi-modal data integration and secure multi-site collaboration.

Purpose of the Study:

To design a computational system for ontology-anchored query and integration of structured data, narrative text, and images.
To enable multi-site research through Grid services-based electronic data interchange.
To address security and patient confidentiality concerns in handling personal health information.

Main Methods:

Development of an ontology-anchored computational system.
Implementation of Grid services for electronic data interchange.
Integration of a software framework for security and confidentiality.

Main Results:

The system facilitates the query and integration of multi-modal clinical data.
Grid services enable seamless data exchange across multiple research sites.
The software framework effectively manages security and patient confidentiality.

Conclusions:

The designed system supports the concept-oriented query and integration of correlated clinical data for cancer research.
The approach enhances data accessibility and usability for secondary research purposes.
This framework provides a secure and efficient solution for multi-site, multi-modal clinical data research.