The integration of artificial intelligence (AI) systems into clinical practice requires a heavy reconsideration of legal categories related to medical liability, not only nationally but also within the increasingly interconnected European framework. This article examines the transition from the traditional lex artis, rooted in national jurisdictions, to a new paradigm shaped by EU governance. Starting from the Italian regulatory framework established by Law No. 24/2017 (“Gelli-Bianco”), the analysis then turns to Regulation (EU) 2024/1689 (the AI Act), which classifies AI systems in healthcare as “high-risk” and imposes obligations of transparency and human oversight. A comparative perspective is then developed, focusing on Italy, Germany, France, and Spain, and revealing different conceptions of the standard of care: from the German fachärztlicher Standardto the Spanish lex artis ad hoc, through the French dualism between fault-based liability and “national solidarity”. Special attention is devoted to explainable artificial intelligence (XAI) as a means to mitigate the opacity of black-box models, together with the implications of the new directive on liability for defective products. The article further explores emerging scenarios from human-machine interaction, such as concordant error and dissent from algorithmic recommendations, assessing them against national approaches. The study reveals a legal landscape in profound transformation, where technological innovation acts as a catalyst for change in diverse national legal cultures, and concludes by sketching a roadmap for governing the possible convergence toward a European model of “augmented medical liability”, in which harmonized EU principles are interwoven with national legal traditions, seeking equilibrium between technological innovation, patient safety, and legal certainty.

From Guidelines to Algorithms: How AI is Rewriting the Leges Artis and Medical Liability in Europe

Mario Caterini
;
Antonella Guzzo
;
Marianna Rocca
2026-01-01

Abstract

The integration of artificial intelligence (AI) systems into clinical practice requires a heavy reconsideration of legal categories related to medical liability, not only nationally but also within the increasingly interconnected European framework. This article examines the transition from the traditional lex artis, rooted in national jurisdictions, to a new paradigm shaped by EU governance. Starting from the Italian regulatory framework established by Law No. 24/2017 (“Gelli-Bianco”), the analysis then turns to Regulation (EU) 2024/1689 (the AI Act), which classifies AI systems in healthcare as “high-risk” and imposes obligations of transparency and human oversight. A comparative perspective is then developed, focusing on Italy, Germany, France, and Spain, and revealing different conceptions of the standard of care: from the German fachärztlicher Standardto the Spanish lex artis ad hoc, through the French dualism between fault-based liability and “national solidarity”. Special attention is devoted to explainable artificial intelligence (XAI) as a means to mitigate the opacity of black-box models, together with the implications of the new directive on liability for defective products. The article further explores emerging scenarios from human-machine interaction, such as concordant error and dissent from algorithmic recommendations, assessing them against national approaches. The study reveals a legal landscape in profound transformation, where technological innovation acts as a catalyst for change in diverse national legal cultures, and concludes by sketching a roadmap for governing the possible convergence toward a European model of “augmented medical liability”, in which harmonized EU principles are interwoven with national legal traditions, seeking equilibrium between technological innovation, patient safety, and legal certainty.
2026
artificial intelligence; medical liability; clinical guidelines; explainable artificial intelligence (XAI)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/404028
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