Overview
The World Health Organization defines pharmacovigilance (PV) as “the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem.”
The goals of PV are to bolster patient safety concerning medicine use by providing a system to collect, assess, and distribute drug safety data. PV activities involve monitoring approved drugs and investigational medicinal products (IMPs) to:
- Identify previously unknown adverse effects
- Recognize changes in the frequency or severity of known adverse effects
- Assess a drugs risk/benefit to determine if action is required to improve safety
- Ensure the accuracy of information communicated to healthcare professionals and patients, and to ensure information contained in patient information leaflets (PILs) is up to date
Artificial intelligence in pharmacovigilance
AI technologies in PV are very helpful in the extraction of accurate information. AI tools can automate or facilitate almost every aspect of PV in case processing, risk tracking, which reduces the total processing time.
- The most important benefits of AI are reduced cycle times. Due to this method, the processing is spontaneous
- Improve the quality and accuracy of the information
- AI can handle or manage diverse types of incoming data formats
- It can be used for the identification of ADRs
- AI is useful to reduce the burden and time of case processing
- AI tools extract the information from the adverse drug event form and evaluate the case validity without the workforce.
There are many applications of AI in PV, and it is bound to have an economic impact on the PV field.
Artificial intelligence in pharmacovigilance
Over the past few years, there has been a drastic increase in data digitalization in the pharmaceutical sector. However, this digitalization comes with the challenge of acquiring, scrutinizing, and applying that knowledge to solve complex clinical problems. This motivates the use of AI, because it can handle large volumes of data with enhanced automation. AI is a technology-based system involving various advanced tools and networks that can mimic human intelligence. At the same time, it does not threaten to replace human physical presence completely. AI utilizes systems and software that can interpret and learn from the input data to make independent decisions for accomplishing specific objectives.
AI can be used effectively in different parts of drug discovery, including drug design, chemical synthesis, drug screening, polypharmacology, and drug repurposing.