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What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach.

Elisabeth Hildt1

  • 1Center for the Study of Ethics in the Professions, Illinois Institute of Technology, Chicago, IL 60616, USA.

Bioengineering (Basel, Switzerland)
|April 26, 2025
PubMed
Summary

Explainability in medical artificial intelligence (AI) clinical decision support systems (CDSSs) is crucial but varies by application. More research is needed on clinician and patient perspectives regarding AI explainability.

Keywords:
artificial intelligence (AI)automation biasautonomyclinical decision support system (CDSS)doctor–patient relationshipethicsexplainable AIinformed consentmachine learningmedical decision making

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Area of Science:

  • Medical Artificial Intelligence
  • Health Informatics
  • Clinical Decision Support Systems

Background:

  • Explainability is a key consideration for AI in healthcare, particularly for clinical decision support systems (CDSSs).
  • Understanding the role and implications of explainability is vital for the safe and effective deployment of AI in medical settings.

Purpose of the Study:

  • To reflect on the concept of explainability in medical AI, specifically within AI-based CDSSs.
  • To analyze the role, relevance, and implications of explainability through diverse use cases.
  • To identify gaps in empirical data and propose future research directions.

Main Methods:

  • Introduction to explainability in AI and overview of AI-based CDSSs.
  • Presentation of four distinct use cases of AI-based CDSSs, showcasing different explanation types (black-box, post-hoc, hybrid, causal).
  • Discussion of seven key themes related to explainability: addressees, decision-making relevance, explanation type, accuracy trade-offs, automation bias, individual values, and patient autonomy.

Main Results:

  • The relevance and role of explainability in AI-based CDSSs are highly context-dependent.
  • The study highlights a scarcity of empirical data on explainability and its implications in medical AI.
  • A potential conflict between explainability and accuracy, alongside issues of epistemic authority and automation bias, are noted.

Conclusions:

  • Explainability in medical AI-based CDSSs is important but its impact varies significantly across different tools and contexts.
  • Further use-case-based research is essential to understand both technical aspects and the perspectives of clinicians and patients.
  • Empirical investigation into the implications of explainability is needed to guide future development and implementation.