Artificial intelligence in healthcare: Proposal for a new medico-legal methodology in medical liability

Abstract

The rapid integration of Artificial Intelligence (AI) into healthcare promises significant benefits but also raises unprecedented ethical, clinical, and legal challenges. Current medico-legal frameworks, primarily designed for human decision-making, are often inadequate to address liability issues arising from algorithmic errors or opaque "black box" models. This paper introduces a novel medico-legal methodology that combines proactive and reactive approaches to risk assessment, originally developed within European forensic medicine, and adapts it to the context of AI in healthcare. By systematically analyzing data collection, dataset validation, error identification, and causal reconstruction, the proposed framework provides a structured path for evaluating medical liability when AI systems are involved. This dual approach not only supports clinicians, developers, and policymakers in preventing harm, but also establishes a robust forensic tool for liability assessment. The methodology offers a step toward internationally applicable standards for addressing the medico-legal implications of AI in medicine.

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