Artificial Intelligence in Pharmacovigilance - Kumar, Dinesh; Kaur, Amandeep; Shruti1; Kaur, Davender (Current Computer Science, 2025)
Pharmacovigilance (PV) is a data-driven method that quickly identifies medication safety risks by processing reports of suspected Adverse Events (AEs) and extracting health data. The first steps in the PV case processing cycle include data collection, data entry, coding, preliminary validity and completeness checks, and medical evaluation for severity, seriousness, expectation, and causality. Afterward, a report is submitted, quality is checked, and data storage and maintenance are performed. This process is costly and time-consuming, as it requires both a workforce and technology. Conversely, artificial intelligence (AI) is used to reduce this time investment and increase data accuracy. AI includes machine learning methods like deep learning and natural language processing, which can recognize and retrieve information on adverse drug occurrences. By doing so, it is possible to optimize the pharmacovigilance process and improve the tracking of documented adverse medication occurrences. AI's advancement in pharmacovigilance raises concerns about potential changes in drug safety professionals' roles, prompting curiosity about their future in an AI-assisted workplace. Artificial Intelligence (AI) should augment human intelligence, not replace human specialists. It's crucial to highlight and ensure AI improves PV more than it causes problems. The pharmaceutical business faces significant obstacles and opportunities, especially when it comes to implementing and employing advanced Information Technology (IT) in Pharmaceutical Monitoring Systems (PMS). Automation improves PV in several ways (e.g., boosting data quality or improving consistency). Several themes are discussed, outlining the challenges encountered, exploring potential solutions, and emphasizing the need for further research. The accepted use case involves automating the workflow in the case of ICRS.
Keywords: health data