ABSTRACT
The field of pharmacovigilance is the practice of monitoring and assessing the safety of drugs and medical devices. The major barriers to reporting adverse drug reactions (ADR) in India include lack of training in ADR identification, clinician attitudes concerning reporting, difficulties setting up reporting systems in hospitals, and a lack of understanding and awareness among health professionals. About 90% of ADRs go unreported. AI can increase ADR reporting rate by mining unreported ADEs documented in medical papers and Electronic Health Record. AI is being utilized to automate ICSR and improve pharmacovigilance by efficiently retrieving medical and clinical data from free text to extract information from adverse event reports and save it in a patient safety database with improved accuracy. AI technologies play a crucial role in pharmacovigilance by automating the examination of large volumes of data, such as adverse event reports and medical literature, to identify potential safety signals. By leveraging ML and NLP, pharmacovigilance professionals can efficiently detect and assess drug safety concerns, ultimately improving patient safety and regulatory compliance. AI has occurred as a favorable technique for improving pharmacovigilance processes. This systematic review will look at the role of AI in pharmacovigilance, its advantages over traditional methods, and the limitations and obstacles of integrating AI.
