Review
Exp. Biol. Med.
Sec. Artificial Intelligence/Machine Learning Applications to Biomedical Research
Volume 250 - 2025 | doi: 10.3389/ebm.2025.10555
This article is part of the IssueBreakthroughs in Biomedical Research: Review/Minireview IssueView all 5 articles
Pharmacovigilance in the Digital Age: Gaining Insight from Social Media Data
- United States Food and Drug Administration, Silver Spring, Maryland, United States
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Pharmacovigilance is essential for protecting patient health by monitoring and managing medication-related risks. Traditional methods like spontaneous reporting systems and clinical trials are valuable for identifying adverse drug events, but face delays in data access. Social media platforms, with their real-time data, offer a novel avenue for pharmacovigilance by providing a wealth of user-generated content on medication usage, adverse drug events, and public sentiment.However, the unstructured nature of social media content presents challenges in data analysis, including variability and potential biases. Advanced techniques like natural language processing and machine learning are increasingly being employed to extract meaningful information from social media data, aiding in early adverse drug event detection and real-time medication safety monitoring. Ensuring data reliability and addressing ethical considerations are crucial in this context. This review examines the existing literature on the use of social media data for drug safety analysis, highlighting the platforms involved, methodologies applied, and research questions explored. It also discusses the challenges, limitations, and future directions of this emerging field, emphasizing the need for ethical principles, transparency, and interdisciplinary collaboration to maximize the potential of social media in enhancing pharmacovigilance efforts.
Keywords: drug safety, Artificial intelligence, machine learning, natural language processing, Social media, post-market surveillance
Received: 28 Feb 2025; Accepted: 09 May 2025.
Copyright: © 2025 Dong, Guo, LIU, Patterson and Hong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Huixiao Hong, United States Food and Drug Administration, Silver Spring, 20993, Maryland, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.