Neurosurgery, Explainable AI, and Legal Liability

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.