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

Autor: François-Henri Boissel, Anny Tirel, Anne Metzinger, Vincent Diebolt, William Saurin, Isaac Azancot, Christine Balagué, Emannuelle Voisin, Thomas Roche, Nacer Boubenna, Isabelle Adenot, Pierre Philip, Françoise Lethiec, J Longin, Emmanuel Pham, Enguerrand Habran, Hélène Coulonjou, Thierry Marchal, Yvon Merlière, Philippe Barthélémy, Xosé M Fernández
Přispěvatelé: CCSD, Accord Elsevier, Plateforme F-CRIN, Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital Lariboisière, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Lariboisière-Fernand-Widal [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Diderot - Paris 7 (UPD7), Novadiscovery [Lyon], Haute Autorité de Santé [Saint-Denis La Plaine] (HAS), Département Management, Marketing et Stratégie (IMT-BS - MMS), Télécom Ecole de Management (TEM)-Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom Business School (IMT-BS), Institut Mines-Télécom [Paris] (IMT), Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) (LITEM), Université d'Évry-Val-d'Essonne (UEVE)-Institut Mines-Télécom Business School (IMT-BS), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), AstraZeneca, Inserm-Transfert [Paris], Institut National de la Santé et de la Recherche Médicale (INSERM), Délégation de la Recherche Clinique et de l’Innovation [Paris] (DRCI), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut Curie [Paris], Fédération Hospitalière de France (FHF), Janssen-Cilag [Issy-les-Moulineaux], Merck Santé, Merck & Co. Inc, Hospices Civils de Lyon (HCL), Caisse nationale d'assurance maladie des travailleurs salariés [CNAMTS], IPSEN, IPSEN Research Laboratories, CHU de Bordeaux Pellegrin [Bordeaux], DELSOL Avocats (.), Dassault Systèmes, MSD, Voisin Consulting Life Sciences (.) (VCLS), Université Catholique de Louvain = Catholic University of Louvain (UCL), LITEM-NPR, Université de Toulouse (UT)-Université de Toulouse (UT)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM), Département Management, Marketing et Stratégie (MMS), Délégation Recherche Clinique et Innovation (DRCI), ANSYS (Université Catholique de Louvain), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Hôpital Purpan [Toulouse], CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Mines-Télécom Business School (IMT-BS), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université d'Évry-Val-d'Essonne (UEVE)
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Biomedical Research
Computer science
Process (engineering)
As is
Mindset
Context (language use)
Assessment
030226 pharmacology & pharmacy
Real-life studies
Clinical research
03 medical and health sciences
Appropriation
0302 clinical medicine
Clinical trials
Interdisciplinary
Artificial Intelligence
Health care
Humans
Training
Pharmacology (medical)
Quality of Health Care
Clinical Trials as Topic
Governance
Data
Data collection
business.industry
Human intelligence
Information Dissemination
Research
Interoperability
3. Good health
Knowledge
[SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology
[SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology
[SHS.GESTION]Humanities and Social Sciences/Business administration
Artificial intelligence
Ergonomics
France
business
[SHS.GESTION] Humanities and Social Sciences/Business administration
Zdroj: Thérapie
Thérapie, 2019, 74 (1), pp.155-164. ⟨10.1016/j.therap.2018.12.003⟩
Thérapie, EDP Sciences, 2019, 74 (1), pp.155-164. ⟨10.1016/j.therap.2018.12.003⟩
ISSN: 1958-5578
0040-5957
DOI: 10.1016/j.therap.2018.12.003⟩
Popis: International audience; 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.
Databáze: OpenAIRE
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