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Related Experiment Video

Updated: Jun 1, 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

Conversational case-based reasoning in medical decision making.

David McSherry¹

¹School of Computing and Information Engineering, University of Ulster, Coleraine BT52 1SA, Northern Ireland, United Kingdom. dmg.mcsherry@ulster.ac.uk

Artificial Intelligence in Medicine

|May 24, 2011

Summary

This summary is machine-generated.

This study introduces iNN(k), a conversational case-based reasoning (CCBR) algorithm for medical diagnosis. iNN(k) enhances transparency and efficiency by selecting features crucial for confirming a target class, improving diagnostic accuracy.

Related Experiment Videos

Last Updated: Jun 1, 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:

Artificial Intelligence
Medical Informatics
Machine Learning

Background:

Conversational case-based reasoning (CCBR) in medicine faces challenges balancing solution quality, efficiency, and transparency.
Explaining the relevance of requested information (e.g., test results) is difficult without a clear diagnostic hypothesis.

Purpose of the Study:

To present an approach for CCBR in medical classification and diagnosis that improves transparency, accuracy, and efficiency.
To address the trade-off between solution quality, problem-solving efficiency, and transparency in medical CCBR applications.

Main Methods:

An algorithm named iNN(k) was developed for CCBR, where feature selection is driven by the goal of confirming a target class.
Feature selection in iNN(k) is informed by a measure of the feature's discriminating power for the target class.
A CCBR system, CBR-Confirm, was built using the iNN(k) algorithm to demonstrate its explainability.

Main Results:

The performance of iNN(k) is dependent on the parameter k and the choice between local or global feature selection.
Optimal parameter combinations were identified for specific datasets to balance accuracy and efficiency.
For instance, iNN(k) achieved 86.5% and 84.3% accuracy on the lymphography and SPECT heart datasets, respectively, using only 42% and 51% of the features.

Conclusions:

The iNN(k) algorithm demonstrates high accuracy across various medical datasets.
It requires users to provide only a subset of features, enhancing efficiency.
The approach enables CCBR systems to explain the relevance of all questions posed to the user.