As artificial intelligence becomes a powerful force in surgical planning and decision-making, neurosurgeons face a profound ethical dilemma:

What happens when the machine gets it wrong?

In this solo episode of Deeply Unqualified, Aureliana Toma explores the legal, ethical, and medical consequences of integrating AI into the operating room.

We dive into:
• AI-powered brain tumor diagnosis (85% accuracy – McGill, 2025)
• Robotic surgery platforms like Mazor X and ExcelsiusGPS
• Real cases of surgical robots causing complications
• The concept of “inhuman errors” missed by AI but not by humans
• The legal gray zone: Who is liable when AI fails—surgeon, developer, or no one?
• Canada's regulatory response to AI in healthcare
• The tension between human judgment and machine autonomy

Featuring data from peer-reviewed studies, FDA reports, and leading institutions like McGill University, Duke, and Health Canada.

Key Topics Covered:

• AI in glioma grading and brain tumor classification

• Deep learning for neurosurgical diagnostics

• Surgical robots and intraoperative precision

• Algorithmic bias and health equity

• Informed consent in the age of AI

• Brain shift and reference frame error in neurosurgery

• The evolving definition of surgical expertise

What do you think? Should AI ever hold surgical responsibility? Drop your thoughts in the comments.

Follow on Instagram: @neurosurgerytales

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