Healthcare AI 2025

FRANCE Law and Practice Contributed by: Liliana Eskenazi, Julie Ernewein and Pauline Lecrenais, Fréget Glaser et Associés

These include identifying and assessing foreseeable risks to health, safety, or fundamental rights; imple - menting effective controls; reducing or eliminating risks; and reporting serious incidents. Risk Assessment Processes for Developers and Healthcare Institutions Developers must establish and document a risk-man - agement system for high-risk AI. Healthcare institu - tions must assess clinical risks before deployment, ensure proper use of AI tools through technical and organisational measures, and provide adequate train - ing for staff to interpret AI results responsibly. 4.4 Defences and Limitations Defences Available to Healthcare Professionals Healthcare professionals may avoid liability if they can demonstrate that they have used AI tools con - scientiously, competently, and in line with accepted medical standards. French law maintains the principle that clinical judgment must prevail. Practitioners must evaluate AI-generated results critically and remain ful - ly responsible for their decisions. Liability may be avoided if the clinician can show: • appropriate and informed use of the AI tool; • acceptance or rejection of AI recommendations based on their clinical judgment; • that the patient was informed of AI involvement in their care; and • that human oversight and standard care protocols were respected. Defences and Limitations for AI Developers and Manufacturers Developers may invoke regulatory compliance – such as CE marking, ISO standards and obligations under the AI Act and PLD – as part of their defence in prod - uct liability claims. While not exempting them from liability, such compliance may mitigate responsibility or demonstrate due diligence. Absence of Safe Harbour Provisions French and EU law currently provide no explicit safe harbour for healthcare providers or AI developers. Certification or regulatory approval does not exempt them from liability in cases of harm caused by misuse,

malfunction or oversight failures. Regulatory compli - ance does not replace the duty of care. Challenges Posed by the “Black-Box” Nature of Some AI Systems The opaque nature of some AI models creates chal - lenges in liability litigation, including: • difficulty in tracing how results were generated; • unclear attribution of responsibility between AI and user; and • obstacles in proving causation or fault when decision-making processes cannot be explained. Because legal frameworks require proof of fault, dam - age and causality, the lack of “explainability” compli - cates the patient’s burden of proof. 5. Ethical and Governance Considerations for Healthcare AI 5.1 Ethical Frameworks Ethical Frameworks Governing Healthcare AI in France Digital ethics in healthcare promotes values such as trust, transparency and fairness in AI use, evolving in a fast-changing environment. International and European Foundations At international level: • OECD Principles (2019) support human-centric, safe, fair and accountable AI; • UNESCO AI Ethics (2021) set global standards focused on human rights and data protection; • GDPR enforces data privacy and user rights; • MDR ensures safety, transparency and traceability of AI-based medical devices; and • the AI Act imposes risk-based rules and strict obli - gations for high-risk AI systems. National Framework French bioethics law, notably Article L.4001-3 of the French Public Health Code, reinforces these princi - ples.

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