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Related Experiment Video

Updated: May 29, 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 framework for evaluating evidence in evidence-based design.

Debajyoti Pati¹

¹Center for Advanced Design Research & Evaluation, Dallas, TX, USA. dpati@hksinc.com

|August 26, 2011

Summary

This summary is machine-generated.

Determining evidence credibility is crucial for evidence-based design (EBD). This study proposes a new framework to evaluate evidence strength, quality, appropriateness, and feasibility for broader EBD adoption.

Related Experiment Videos

Last Updated: May 29, 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:

Healthcare design
Evidence-based practice

Background:

A significant barrier to adopting evidence-based design (EBD) is the difficulty in assessing the credibility of evidence.
This challenge hinders the widespread implementation of EBD principles in practice.

Purpose of the Study:

To propose a novel framework for evaluating evidence within the EBD practice model.
To address the limitations in determining evidence credibility for EBD.

Main Methods:

The study draws upon established methodologies from evidence-based medicine and evidence-based practice.
A framework is proposed that distinguishes between evidence evaluation and contextual application.

Main Results:

The proposed framework separates the assessment of evidence strength and quality from its appropriateness and feasibility.
This distinction aims to provide a more nuanced approach to evidence evaluation in EBD.

Conclusions:

Implementing this framework can enhance the credibility assessment of evidence in EBD.
This approach is expected to facilitate the broader adoption and application of evidence-based design principles.