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Related Experiment Video

Updated: Aug 9, 2025

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

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Published on: June 13, 2025

To explain or not to explain?-Artificial intelligence explainability in clinical decision support systems.

Julia Amann¹, Dennis Vetter^2,3, Stig Nikolaj Blomberg⁴

¹Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.

PLOS Digital Health

|February 22, 2023

Summary

Explainability for artificial intelligence (AI) in medicine, specifically AI-powered Clinical Decision Support Systems (CDSS), is crucial. Its value depends on technical feasibility, context, and user needs for effective healthcare decisions.

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Clinical Decision Support Systems

Background:

Explainability in artificial intelligence (AI) is a significant topic in medicine.
AI-powered Clinical Decision Support Systems (CDSS) are increasingly used in clinical settings.
The debate on the necessity and impact of explainability for AI in healthcare is ongoing.

Purpose of the Study:

To review arguments for and against explainability in AI-powered CDSS.
To analyze the role of explainability in a specific use case: an AI-powered CDSS for cardiac arrest identification in emergency calls.
To provide a nuanced account of explainability's value through socio-technical scenarios.

Main Methods:

Normative analysis using socio-technical scenarios.
Focus on three layers: technical considerations, human factors, and system role in decision-making.
Abstraction from a concrete use case to general principles.

Main Results:

The value of explainability for CDSS is contingent on multiple factors.
Key factors include technical feasibility, algorithm validation, implementation context, system's role, and user groups.
An individualized assessment of explainability needs is necessary for each CDSS.

Conclusions:

Explainability's added value is not universal and requires careful consideration.
Context-specific analysis is essential to determine the need for explainability in AI-powered CDSS.
The study provides a framework for assessing explainability requirements in practice.