What place for intelligent automation and artificial intelligence to preserve and strengthen vigilance expertise in the face of increasing declarations?

Therapie. 2023 Jan-Feb;78(1):131-143. doi: 10.1016/j.therap.2022.11.004. Epub 2022 Nov 25.


In 2018, the "Ateliers de Giens" (Giens Workshops) devoted a workshop to artificial intelligence (AI) and led its experts to confirm the potential contribution and theoretical benefit of AI in clinical research, pharmacovigilance, and in improving the efficiency of care. The 2022 workshop is a continuation of this reflection on AI and intelligent automation (IA) by focusing on its contribution to pharmacovigilance and the applications and tasks could be optimized to preserve and strengthen medical and pharmacological expertise in pharmacovigilance. The evolution of pharmacovigilance work is characterized by many tasks with low added value, a growing volume of pharmacovigilance reporting of suspected side effects, and a scarcity of medical staff with expertise in clinical pharmacology and pharmacovigilance and human resources to support this growing need. Together, these parameters contribute to an embolization of the pharmacovigilance system at risk of missing its primary mission: to identify and characterize a risk or even a health alert on a drug. The participants of the workshop (representatives of the Regional Pharmacovigilance Centres (CRPV), the French National Agency for Safety of Medicinal Products (ANSM), patients, the pharmaceutical industry, or start-ups working in the development of AI in the field of medicine) shared their experiences, their pilot projects and their expectations on the expected potential, theoretical or proven, AI and IA. This work has made it possible to identify the needs and challenges that AI or IA represent, in the current or future modes of organization of pharmacovigilance activities. This approach led to the development of a SWOT matrix (strengths, weaknesses, opportunities, threats), a basis for reflection to identify critical points and consider four main recommendations: (1) preserve and develop business expertise in pharmacovigilance (including research and development in methods) with the integration of new technologies; (2) improve the quality of pharmacovigilance reports; (3) adapt technical and regulatory means; (4) implement a development strategy for AI and IA tools at the service of expertise.

Keywords: Artificial automation; Artificial intelligence; Evaluation; Interoperability; Machine learning; Pharmacovigilance; Side effects.

MeSH terms

  • Artificial Intelligence*
  • Automation
  • Drug Industry
  • Drug-Related Side Effects and Adverse Reactions* / epidemiology
  • Drug-Related Side Effects and Adverse Reactions* / prevention & control
  • Humans
  • Pharmacovigilance