Next-Generation Pharmacovigilance: The Role of AI and Machine Learning in Detecting and Managing Drug Risks
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Abstract
The increase in the size and complexity of drug safety information is increasingly pushing the traditional pharmacovigilance systems towards failure in timely detecting adverse drug reactions, as well as ineffective signal identification. Machine learning (ML) and artificial intelligence can bring revolutionary prospects to advance drug safety monitoring with its automated data processing, recognition of the patterns, and predictive analytics. The review focuses on the assessment of existing AI and ML uses, advantages, and drawbacks in pharmacovigilance. PubMed, Scopus, and Web of Science were selected as the databases that underwent a systematic literature search of the publications published between 2020 and 2025 using the following key words: pharmacovigilance, AI, ML, ADRs, and drug safety monitoring. Relevant publications have been analysed to determine the important technological methods, regulatory factors, and barriers to the implementation. The AI-based systems show a high level of enhancement in ADR detection rates, signal control, and automatic processing of cases. The practicality of natural Text analysis for extraction safety data in unstructured clinical stories and social media and the ability of Ml-based models to improve predictive risk stratification were demonstrated. Deep-learning solutions have a specific potential in the use of electronic health records and real-world evidence. There are still issues of data standardization, transparency of the algorithms, compliance with the regulation, and the integration with the current workflows. The adoption should be accompanied by sound validation structures and alignment with changing regulatory rules in order to succeed.
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