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Related Experiment Video

Updated: May 15, 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

Knowledge translation in rehabilitation engineering research and development: a knowledge ecosystem framework.

Tom Chau¹, Saba Moghimi, Milos R Popovic

¹Institute of Biomaterials and Biomedical Engineering, Faculty of Medicine, University of Toronto, Toronto, ON, Canada. tom.chau@utoronto.ca

Archives of Physical Medicine and Rehabilitation

|December 25, 2012

Summary

Rehabilitation engineering research requires a complete knowledge translation (KT) cycle, moving from need to product. This study proposes a knowledge ecosystem to guide this challenging process for technology innovations.

Related Experiment Videos

Last Updated: May 15, 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

Area of Science:

Rehabilitation Engineering
Biomedical Engineering
Health Technology

Background:

Rehabilitation engineering focuses on technology for individuals with disabilities.
The knowledge translation (KT) cycle in this field is complex, involving multiple sectors and stakeholders.
Successful KT requires a technology product or therapy to reach end-users.

Purpose of the Study:

To propose a knowledge ecosystem framework for rehabilitation engineering research and development (R&D).
To map the knowledge flow from identifying needs to developing technology products.
To guide the challenging KT process in rehabilitation engineering.

Main Methods:

Literature review on the knowledge trichotomy in technology-based rehabilitation R&D.
Development of a conceptual knowledge ecosystem model.
Analysis of knowledge management principles and knowledge flow.
Case studies of two local rehabilitation engineering R&D commercialization projects.

Main Results:

A proposed knowledge ecosystem frames the rehabilitation engineering KT process.
Identified key roles of repository and networked knowledge.
Defined consumers and producers within a trinity of communities of practice.
Described various knowledge flows using knowledge management literature.

Conclusions:

The proposed knowledge ecosystem provides a structure for rehabilitation engineering KT.
Understanding knowledge flow is crucial for successful technology development and implementation.
The framework can guide future rehabilitation engineering R&D commercialization efforts.