Ethical, Legal, and Professional Vulnerabilities: Healthcare Providers' Perceptions of Liability in AI-Assisted Surgical and Diagnostic Protocols

Background

The rise of clinical algorithmic tools has transformed healthcare delivery, establishing artificial intelligence protocols as pervasive standards in diagnostic and surgical workflows. Despite their diagnostic precision and operational efficiency, empirical investigation into the professional, legal, and epistemic vulnerabilities experienced by frontline clinicians under prevailing liability doctrines remains limited.