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Related Concept Videos

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Criteria for Causality: Bradford Hill Criteria - II01:28

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Criteria for Causality: Bradford Hill Criteria - I01:30

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The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
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Updated: Aug 3, 2025

Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues
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Explainability and causability in digital pathology.

Markus Plass1, Michaela Kargl1, Tim-Rasmus Kiehl2

  • 1Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria.

The Journal of Pathology. Clinical Research
|April 12, 2023
PubMed
Summary
This summary is machine-generated.

Explainable AI (XAI) in digital pathology is crucial for understanding AI decisions. Novel interfaces are needed to complement XAI, ensuring medical experts maintain control and causal understanding in AI-driven diagnostics.

Keywords:
artificial intelligencecausabilitydigital pathologyexplainability

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Area of Science:

  • Digital pathology and artificial intelligence (AI) applications.
  • Machine learning and explainable AI (XAI) in medical diagnostics.

Background:

  • AI algorithms in digital pathology often function as "black boxes," hindering understanding of their diagnostic reasoning.
  • Lack of transparency in AI decision-making poses risks in medicine, necessitating clear explanations for patient safety and trust.

Purpose of the Study:

  • To provide medical experts with insights into explainability challenges in digital pathology.
  • To explore the role of explainable AI (XAI) and propose complementary solutions for AI in pathology.

Main Methods:

  • Review of core machine learning concepts relevant to AI explainability.
  • Discussion of existing XAI techniques and their limitations in medical contexts.
  • Argument for the necessity of explanation interfaces to enhance AI understanding.

Main Results:

  • Current XAI methods offer a foundational level of transparency for AI models.
  • An explanation interface is essential to achieve high causability and make AI results actionable for medical professionals.
  • Explainability and causability are vital for regulatory compliance in medical AI.

Conclusions:

  • Novel user interfaces are required for AI in pathology to enable contextual understanding and interactive "what-if" analyses.
  • These interfaces are critical for maintaining the "human-in-the-loop" and integrating expert knowledge into AI processes.
  • Enhancing causability and expert involvement ensures safer and more effective AI implementation in pathology.