Substantial Modification of Medical AI and Product Liability

Ampovska, Marija (2026) Substantial Modification of Medical AI and Product Liability. In: 21st Joint Seminar of EALE and The Geneva Association Legal and economic issues in emerging liability risk, 18-19 June 2026, Jesus College, Cambridge University. (Unpublished)

[thumbnail of 21st Joint Seminar Programme [v2].pdf] Text
21st Joint Seminar Programme [v2].pdf - Other

Download (152kB)
[thumbnail of ppt za repozitorium.pdf] Text
ppt za repozitorium.pdf - Presentation

Download (206kB)

Abstract

The revised Product Liability Directive (rPLD) introduces a pivotal shift in the liability landscape for healthcare professionals using artificial intelligence. While existing research often focuses on professional or regulatory thresholds, this paper highlights a critical and underexplored juncture: the moment a healthcare professional’s interaction with an AI system constitutes a substantial modification, thereby transforming them from a user into a de facto producer under the rPLD’s strict liability regime.

Drawing on the author’s prior work on threshold-based liability frameworks, this paper examines how the rPLD redefines the “product” to include software and AI systems, and expands liability to any natural or legal person who substantially modifies a product outside the manufacturer’s control. In clinical practice, such modifications may occur through actions such as overriding safety parameters, integrating unauthorized components, or using certified AI outside its intended purpose. When these actions alter the safety baseline of an AI-enabled medical device, the healthcare professional crosses the product liability threshold, triggering strict liability for any resulting harm.

The analysis situates this threshold within the broader EU regulatory ecosystem—including the AI Act, the proposed AI Liability Directive, the GDPR, and the Medical Devices Regulation—to demonstrate how liability becomes distributed and context-dependent. The paper argues that the rPLD does not merely complement existing fault-based regimes but creates a distinct, strict liability pathway that reallocates risk to the party with the highest degree of control over the AI system’s final safety configuration.

By clarifying the doctrinal and practical implications of the “substantial modification” clause, this research provides a needed framework for legal scholars, insurers, and healthcare practitioners to navigate emerging liability risks. It also contributes to ongoing policy discussions on how to balance innovation in medical AI with robust patient protection and clear accountability structures.

Keywords: substantial modification, revised Product Liability Directive, medical AI, healthcare professionals, strict liability, EU AI law

Item Type: Conference or Workshop Item (Paper)
Subjects: Social Sciences > Law
Divisions: Faculty of Law
Depositing User: Marija Radevska
Date Deposited: 29 Jun 2026 09:59
Last Modified: 29 Jun 2026 09:59
URI: https://eprints.ugd.edu.mk/id/eprint/38586

Actions (login required)

View Item
View Item