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"“Artificial intelligence”: Which services, which applications, which results and which development today in clinical research? Which impact on the quality of care? Which recommendations?"

by Diebolt, Vincent; Azancot, Isaac; Boissel, François-Henri; Adenot, Isabelle; Balague, Christine; Barthélémy, Philippe; Boubenna, Nacer; Coulonjou, Hélène; Fernandez, Xosé; Habran, Enguerrand; Lethiec, Françoise; Longin, Juliette; Metzinger, Anne; Merlière, Yvon; Pham, Emmanuel; Philip, Pierre; Roche, Thomas; Saurin, William; Tirel, Anny; Voisin, Emmanuelle; Marchal, Thierry (2019)


Summary Artificial intelligence (AI), beyond the concrete applications that have already become part of our daily lives, makes it possible to process numerous and heterogeneous data and knowledge, and to understand potentially complex and abstract rules in a manner human intelligence can but without human intervention. AI combines two properties, self-learning by the successive and repetitive processing of data as well as the capacity to adapt, that is to say the possibility for a scripted program to deal with multiple situations likely to vary over time. Roundtable experts confirmed the potential contribution and theoretical benefit of AI in clinical research and in improving the efficiency of patient care. Experts also measured, as is the case for any new process that people need to get accustomed to, its impact on practices and mindset. To maximize the benefits of AI, four critical points have been identified. The careful consideration of these four points conditions the technical integration and the appropriation by all actors of the life science spectrum: researchers, regulators, drug developers, care establishments, medical practitioners and, above all, patients and the civil society. 1st critical point: produce tangible demonstrations of the contributions of AI in clinical research by quantifying its benefits. 2nd critical point: build trust to foster dissemination and acceptability of AI in healthcare thanks to an adapted regulatory framework. 3rd critical point: ensure the availability of technical skills, which implies an investment in training, the attractiveness of the health sector relative to tech-heavy sectors and the development of ergonomic data collection tools for all health operators. 4th critical point: organize a system of governance for a distributed and secure model at the national level to aggregate the information and services existing at the local level. Thirty-seven concrete recommendations have been formulated which should pave the way for a widespread adoption of AI in clinical research. In this context, the French “Health data hub” initiative constitutes an ideal opportunity.


Artificial Intelligence, Data, Knowledge, Clinical Research, Clinical Trials, Governance, Mental Health


Employment, AI and Science, AI and Medicine, AI and Counselling, Automation

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