Accessibility settings

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

Chatbots and Conversational Agents (1161) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (1017) Clinical Information and Decision Making (3662) Design of Processes and Workflows (282) Ontologies, Classifications, and Coding (434) Pharmacovigilance (243) Artificial Intelligence (4705) Generative Language Models Including ChatGPT (1492)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65651, first published .
Doctor typing on laptop with stethoscope nearby, medical technology

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 (8) 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 2026;81(2):171 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
  4. Salvo F, Crupi L, Cholle C. Language models in pharmacovigilance: Applications, promises and limits. Therapies 2026;81(2):140 View
  5. Chen X, Jin P, Mai H, Liang S, Ping Y, Wang C, Zhao Y. Artificial intelligence in pharmaceutical administration and clinical pharmacy: A comprehensive review of advancements, challenges, and future directions. Intelligent Pharmacy 2026;4(3):235 View
  6. Barus R, Schiro P, Faillie J, Freppel R, Béné J, de Canecaude C, Montastruc F. Applying machine learning to pharmacovigilance data: A proof‐of‐concept study. British Journal of Clinical Pharmacology 2026 View
  7. Kim T, Oh W, Jeong D. Large Language Models in Adverse Drug Reaction Detection and Pharmacovigilance: A Systematic Review of Current Applications, Challenges, and Future Directions. Diagnostics 2026;16(15):2435 View
  8. Khan S, Samuelsson J, Cai X, Natarajan K, Madhavan S. Can agentic AI dent Eroom’s Law? Large reasoning models across the drug discovery and development pipeline. Drug Discovery Today 2026:104754 View