This paper is in the following e-collection/theme issue:

Chatbots and Conversational Agents (748) Generative Language Models Including ChatGPT (725) Artificial Intelligence (2162) Ontologies, Classifications, and Coding (332) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (703) Pharmacovigilance (171) Clinical Information and Decision Making (1935) Design of Processes and Workflows (193)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65651, first published .
Assessment of the Efficiency of a ChatGPT-Based Tool, MyGenAssist, in an Industry Pharmacovigilance Department for Case Documentation: Cross-Over Study

Assessment of the Efficiency of a ChatGPT-Based Tool, MyGenAssist, in an Industry Pharmacovigilance Department for Case Documentation: Cross-Over Study

Assessment of the Efficiency of a ChatGPT-Based Tool, MyGenAssist, in an Industry Pharmacovigilance Department for Case Documentation: Cross-Over Study

Authors of this article:

Alexandre Benaïche1 Author Orcid Image ;   Ingrid Billaut-Laden1 Author Orcid Image ;   Herivelo Randriamihaja1 Author Orcid Image ;   Jean-Philippe Bertocchio2, 3 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Vieira-Vieira C, Kulkarni S, Zalewski A, Löffler J, Münch J, Kreuchwig A. From data silos to insights: the PRINCE multi-agent knowledge engine for preclinical drug development. Frontiers in Artificial Intelligence 2025;8 View
  2. Martel M, José-Garcia A, Vens C, De Vos M, Sobanski V. Artificial intelligence for precision medicine. Therapies 2025 View
  3. Roemming H, Hauben M, Wannhoff W, Schaffer C, Tihaa I, Heitmann M, Mengling V. How LLMs can advance safety case intake—points to consider and insights from a proof of concept. Therapeutic Advances in Drug Safety 2025;16 View