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Explainable AI (XAI) in healthcare, particularly neurosurgery, faces challenges. Transparency in AI training and validation is more crucial than current XAI techniques for building trust and improving clinical decisions.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Neurosurgery Applications
  • Healthcare Technology Ethics

Background:

  • The "black box" nature of Artificial Intelligence (AI) presents a significant challenge in healthcare, limiting explainability and interpretability.
  • This issue is particularly critical in specialized fields like neurosurgery, where clinical decisions have high stakes.

Purpose of the Study:

  • To investigate the necessity and purpose of explainable AI (XAI) systems in general healthcare and neurosurgery.
  • To evaluate the efficacy of current XAI approaches in achieving desired outcomes such as trust, patient autonomy, and improved clinical decision-making.
  • To assess the role of XAI in determining liability for AI-driven medical errors.

Main Methods:

  • Exploration of the theoretical and practical implications of AI explainability in a medical context.
  • Analysis of existing XAI techniques and their suitability for healthcare applications.
  • Argumentative review based on the limitations of current XAI and the benefits of alternative transparency measures.

Main Results:

  • Current XAI techniques are not the sole or optimal method for establishing trust in AI systems within healthcare.
  • XAI has limited significance in addressing issues of patient autonomy, clinical decision support, and legal liability.
  • The effectiveness of XAI in achieving key goals is currently insufficient.

Conclusions:

  • Transparency regarding AI system training and validation processes is more effective than XAI in fostering trust and improving clinical practice.
  • Greater emphasis on comprehensive data and model validation reporting is recommended over solely relying on XAI methods.
  • Alternative approaches to transparency are essential for responsible AI implementation in neurosurgery and broader healthcare.