Artificial intelligence in Drug Safety and Pharmacovigilance
Artificial intelligence (AI) is the broad ranging branch of computer science which has infiltrated almost all areas concerned with healthcare and science. The obvious goal being creating smart machines capable of performing tasks that typically require human intelligence and expertise. With AI features like machine learning and deep learning, data scientists and analysts will soon prove to be an indispensable part of Pharmacovigilance in the coming years. Let’s have a look at how AI and Drug Safety interact with each other.
Facilitating Case Intake and Case Creation
For ICSRs, AI can be helpful in triaging the case based on it’s serious assessment and prioritize it for the Safety Associate. The tool of AI can be used to classify the cases as valid or non valid based on the four seriousness criteria. It’s use for expedited reporting has been shown to save time for the safety personnel. Ofcourse, the goal is to enable AI for end-to-end case processing in the coming years.
AI and Safety Data Analysis
AI features in Pharmacovigilance are very helpful in the segregation of accurate information. AI tools can automate or facilitate almost every aspect in case processing, assessing the benefit-risk profile of the drug, tracking similar adverse events and reactions in relation with the said drug or product, which substantially reduces the net processing time. Many organisations are already using AI for quality checks, follow-up processing and medical assessments. AI as an inbuilt tool with its problem-solving proficiency is and will be even more profitable and suitable in the field of PV.
AI and Safety Database
Safety Databases like Argus and ArisG are constantly evolving given the nature of regulatory reporting rules. With abundant data of AEs stored over decades, which is only going to increase in the coming years, it’s only wise to adapt AI and cloud technology while maintaining regulatory compliance and patient safety. Databases like Argus and ArisG would use innovation to detect risks early, respond quickly and thereby increase compliance – all while making use of the already stored data volume. These databases would also self learn experiences which happen in the present for future use.
