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Philosophical Foundations for Explainable AI in Healthcare Explored

Martina Mattioli, Marcello Pelillo· July 1, 2026 View original

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Summary

This paper critically reviews the intersection of philosophy of science and explainable AI (XAI) in health sciences, examining what constitutes an adequate medical explanation. It identifies causality, trust, and epistemic adequacy as central axes for designing robust XAI systems in clinical decision-making.

The increasing use of medical AI faces a significant challenge: the opacity of many machine learning models. While explainable AI (XAI) aims to clarify these predictions, particularly in high-stakes medical contexts, the definition of an "adequate" explanation remains debated. This research bridges a gap by integrating insights from the philosophy of science and medicine into contemporary XAI discussions.The paper argues that existing philosophical frameworks offer necessary conditions for a robust approach to explainability in healthcare. It highlights three key areas: the role of causality in medical reasoning, the epistemic and relational aspects of trust, and the criteria for explanatory adequacy tailored to diverse stakeholder needs.By synthesizing philosophical analysis with current medical AI developments, the study proposes principles for XAI system design. These principles aim to ensure explanations are not only epistemically sound but also align with the practical and knowledge-based requirements of clinical decision-making, thereby enriching the conceptual foundations of medical XAI.

Why it matters

Professionals developing or deploying AI in healthcare need to understand the foundational requirements for trustworthy and effective explanations. This research provides a framework for building XAI systems that meet clinical and ethical standards.

How to implement this in your domain

  1. 1Integrate philosophical principles of causality and trust into XAI design specifications.
  2. 2Develop XAI models that explicitly address the epistemic and relational dimensions of medical trust.
  3. 3Tailor explanation formats and content to the specific pragmatic needs of different healthcare stakeholders (e.g., clinicians, patients, regulators).
  4. 4Collaborate with ethicists and philosophers of science to refine XAI evaluation criteria for medical applications.

Who benefits

HealthcarePharmaceuticalsMedical DevicesAI Development

Key takeaways

  • Medical AI requires robust explainability grounded in philosophical understanding of explanation.
  • Causality, trust, and epistemic adequacy are critical factors for effective XAI in healthcare.
  • Current XAI research often overlooks foundational philosophical insights, leading to potential gaps.
  • Designing XAI systems that align with clinical decision-making needs philosophical rigor.

Original post by Martina Mattioli, Marcello Pelillo

"arXiv:2606.31616v1 Announce Type: new Abstract: Medical Artificial Intelligence (AI) is widely expected to transform clinical practice, yet the decision-making processes of many Machine Learning (ML) models remain opaque. Explainability has been advanced as a partial remedy to cl…"

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Originally posted by Martina Mattioli, Marcello Pelillo on X · view source

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