AI-Involved Medical Malpractice
Fenella Chadwick.
The increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) tools in clinical decision-making presents a profound challenge to established legal doctrines of medical malpractice. Current liability frameworks are often inadequate to clearly and equitably assign responsibility when patient injury results from an AI-involved error.
Traditional medical negligence focuses on the physician’s deviation from the standard of care. However, the complexity of AI including the “black box” problem of opaque decision-making, the multiplicity of stakeholders (developers, healthcare institutions, and clinicians), and the dynamic, adaptive nature of ML systems fragments responsibility. Potential legal pathways for compensation for patient harm are generally distributed across three main theories:
1. Medical Malpractice/Negligence: Primarily targeting the physician or healthcare provider for negligent use, failure to critically evaluate AI recommendations, or improper implementation.
2. Product Liability: Directed at the AI developer or manufacturer for errors stemming from design defects, manufacturing flaws, or a failure to provide adequate warnings.
3. Vicarious Liability: Holding the healthcare system or hospital accountable for the negligent acts of its employees or affiliates, or for organizational negligence like failing to provide proper training or oversight.
Policy options for a more balanced and forward-looking system include modifying the standard of care to encompass responsible AI use, implementing specialized no-fault adjudication systems, and creating harmonized regulatory frameworks to enhance transparency and accountability across the entire AI supply chain. Without clear definitions of professional responsibilities and legal liability, the fear of accountability may hinder the safe and beneficial adoption of these transformative technologies.
