FRANCE Law and Practice Contributed by: Liliana Eskenazi, Julie Ernewein and Pauline Lecrenais, Fréget Glaser et Associés
particularly in addressing systemic pressures such as financial constraints, healthcare workforce shortages, and population ageing. The main benefits include: • improved clinical outcomes – AI enables earlier and more accurate diagnostics and enhances treatment planning and facilitates personalised medicine; • operational efficiency – waiting times can be reduced and patient pathways streamlined, par - ticularly in hospitals; resource allocation and work - flow management can also be improved; • support for healthcare professionals – routine administrative tasks (eg, documentation, coding, scheduling) are automated and time is freed up for direct patient care, reducing stress and improving job satisfaction; • advancement of medical research – drug discovery and clinical trial design is accelerated, and genera - tive AI contributes by making unexpected connec - tions and enhancing innovation capacity; and • gains are achieved at system level, with efficiency supported across the healthcare system; this can contribute to making medical careers more attrac - tive by reducing the overall non-clinical burden. Challenges Arising From a Healthcare-Specific Perspective Despite its potential, AI deployment in healthcare raises significant concerns, particularly: • data-related risks – data quality, bias and repre - sentativeness; • lack of transparency and “explainability ”–“ black- box” AI systems; • ethical and equity concerns – fears of dehuman - ised medicine due to excessive automation, and the risk of increased health inequalities if AI tools are not equitably deployed across regions; • regulatory and legal complexity – ongoing difficulty in assigning liability in the event of AI-related harm, and the need for coherence across overlapping legal frameworks; and • economic hurdles and barriers to deployment – high implementation costs can deter adoption, particularly when return on investment remains uncertain, with limited reimbursement models
(while individual devices may be reimbursed, broader system-level AI tools often lack dedicated funding pathways). 1.3 Market Trends France’s Supporting Strategy and Investments for AI in Healthcare As part of the Summit for Action on Artificial Intel - ligence in February 2025, the Ministry of Health pub - lished a report on the state of AI in health in France (“ ‘état des lieux de l’intelligence artificielle (IA) en santé en France” ) outlining a comprehensive strategy for AI in healthcare and structured around the four key themes of prevention, care delivery, access to care and supportive framework. According to the report, France’s support of the devel - opment of innovation incorporating AI in healthcare is part of the “Digital Health ”acceleration strategy (SASN) under the“ France 2030” plan. France has so far invested EUR500 million in Digital Health solutions, 50% of which is dedicated to projects involving AI. Key stakeholders are as follows: • healthcare providers – hospitals and clinics adopt - ing AI for clinical decision-making and operational efficiency; • technology companies – such companies develop AI tools tailored to healthcare needs, often in col - laboration with medical institutions; and • regulatory bodies – agencies such as ANSM and HAS assess and certify AI systems and publish guidance to support healthcare stakeholders and companies in implementing AI. HAS has recently communicated its intention to pub - lish several guides pertaining to AI, in parallel with the advancement of a number of ongoing initiatives involving AI applications in the healthcare sector. Notable collaborations between healthcare institu - tions and technology developers are as follows: • PariSanté Campus – a collaborative hub fostering partnerships between public health institutions and private tech companies to accelerate AI innovation in healthcare; and
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