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Related Experiment Video

Updated: Oct 3, 2025

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

Re-focusing explainability in medicine.

Laura Arbelaez Ossa¹, Georg Starke^1,2, Giorgia Lorenzini¹

¹Institute for Biomedical Ethics, University of Basel, Switzerland.

|February 17, 2022

Summary

This summary is machine-generated.

Defining minimal explainability standards for artificial intelligence (AI) in healthcare is crucial. These standards ensure AI is understood by doctors and patients, enabling safe clinical integration.

Keywords:

Explainability digital health explainable AI human-center AI medicine

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Clinical Decision Support Systems

Background:

Artificial intelligence (AI) offers potential to enhance patient care but faces limited clinical adoption due to explainability concerns.
A significant barrier is the lack of a clear definition and consensus on what constitutes sufficient explainability for safe AI use in healthcare.
Disparate terminology and expectations among experts, regulators, and healthcare professionals hinder AI implementation.

Purpose of the Study:

To establish minimal explainability standards for artificial intelligence in clinical settings.
To bridge the gap in understanding AI behavior by defining criteria that meet the needs of doctors, patients, and legal requirements.
To facilitate the safe and ethical integration of AI into routine healthcare practices.

Main Methods:

Literature review and expert consensus-building on AI explainability in healthcare.
Development of a framework for defining context-dependent minimal explainability criteria.
Analysis of stakeholder needs (clinicians, patients, regulators) for AI comprehension.

Main Results:

Proposed definition of minimal explainability as sufficient, not exhaustive, understanding of AI clinical implications.
Identified context-dependent nature of explainability standards based on clinical scenario and potential risks.
Criteria designed to support clinician understanding, patient trust, and regulatory compliance.

Conclusions:

Minimal explainability standards are essential for the responsible and ethical implementation of AI in healthcare.
Context-specific criteria ensure AI models are comprehensible and safe for clinical practice.
Achieving consensus on explainability fosters trust and facilitates wider adoption of AI in patient care.