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Source note3 min read

Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy

Source

arXiv

arxiv.org/abs/2606.31616v1

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Plain-Language Brief

This research paper discusses the need for better understanding and transparency in Artificial Intelligence (AI) used in healthcare, focusing on the explanatory process of Machine Learning models. It highlights that while explainability has been proposed as a solution, there is ongoing debate about what constitutes an adequate explanation in the medical field. The paper bridges the gap between philosophy of science and AI by analyzing prevailing accounts of explanation in the health sciences and arguing for the need for a philosophically grounded approach to explainability in medicine.

What the Document Says

  • Medical AI is expected to transform clinical practice but its decision-making processes are opaque.
  • Explainability has been proposed as a remedy for clarifying AI predictions, particularly in high-stakes contexts.
  • Debates on what constitutes an adequate medical explanation remain unsettled.
  • Philosophical analysis of explanation in science and medicine has been overlooked in contemporary explainable AI research.
  • This paper develops a critical review at the intersection of philosophy of science and explainable AI (XAI) in medicine.
  • The paper examines prevailing accounts of explanation in the health sciences and assesses their adequacy for XAI in medicine.
  • It argues that these accounts provide necessary conditions for a philosophically grounded approach to explainability in medicine.
  • The discussion identifies three central axes: causality, medical trust, and criteria of explanatory adequacy.

Key Concepts Mentioned

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Explainability
  • Philosophy of Science
  • Medical Reasoning
  • Causality
  • Medical Trust
  • Epistemic Adequacy

Claims Made by the Document

  • There is a need for better understanding and transparency in AI used in healthcare.
  • Explainability has been proposed as a remedy for clarifying AI predictions.
  • Debates on what constitutes an adequate medical explanation remain unsettled.
  • Philosophical analysis of explanation in science and medicine is overlooked in contemporary explainable AI research.
  • There is a need for a philosophically grounded approach to explainability in medicine.

Evidence Used

  • The text states that medical AI is expected to transform clinical practice but its decision-making processes are opaque.
  • The text mentions that explainability has been proposed as a solution.
  • The text discusses ongoing debates about what constitutes an adequate medical explanation.
  • The text highlights that philosophy of science and medicine analysis is overlooked in contemporary explainable AI research.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

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Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 clarify why AI generates predictions, particularly in high-stakes contexts. Despite ongoing efforts, debates on what constitutes an adequate medical explanation remain unsettled. Yet, explanation has long been a central topic of inquiry in the philosophy of science and medicine. The insights developed in these fields, however, have been largely overlooked in contemporary explainable AI (XAI) research, leaving its foundational assumptions insufficiently examined. To address this gap, this paper develops a critical review at the intersection of philosophy of science and XAI. It examines prevailing accounts of what counts as an explanation in the health sciences and assesses their adequacy for informing XAI in medicine, arguing that they provide necessary conditions for a philosophically grounded approach to explainability in this domain. Building on this foundational philosophical literature, the discussion identifies three central axes of analysis: the role of causality in medical reasoning, the epistemic and relational dimensions of medical trust, and the criteria of explanatory adequacy as shaped by the pragmatic needs of diverse stakeholders. By integrating philosophical analysis with current developments in medical AI, the paper outlines principles for designing XAI systems that offer explanations that are not only epistemically robust but also aligned with the epistemic and practical requirements of clinical decision-making, shaping ongoing debates in medical XAI toward underexplored conceptual foundations.