Artificial Intelligence in Pharmacovigilance: Leadership for Ethical AI Integration and Human-AI Collaboration in the Pharmaceutical Industry

Purpose Pharmacovigilance plays a vital role in ensuring medication and vaccine safety, yet it faces persistent challenges, including underreporting, resource-intensive processes, and regulatory complexities. Artificial intelligence has the potential to enhance efficiency, but its adoption requires strategic leadership to navigate automation feasibility, ethical dilemmas, and socio-economic implications.Design/methodology/approachThis study uses a systematic review with bibliometric and content analysis to address three core questions: the current state of artificial intelligence in pharmacovigilance, the feasibility of full automation, and the ethical dilemmas associated with its adoption. It explores six themes, including explainable AI, effectiveness, predictive applications, social media-based detection, challenges, and models used.Findings The findings reveal the growing use of AI, especially machine learning and natural language processing, to improve adverse drug reaction detection and streamline pharmacovigilance. Yet, full automation faces barriers like privacy concerns, regulatory gaps, and data biases. A strategic leadership approach, integrating AI-driven efficiency with human expertise, is essential to maintaining patient safety and public trust. Ethical concerns, including transparency, accountability, and fairness, must be addressed through responsible AI governance frameworks.Research limitations/implications The rapid evolution of AI technologies and regulatory frameworks means new insights are increasingly available. Future research should explore leadership strategies, regulatory adaptations, and governance models that ensure ethical and practical AI adoption in pharmacovigilance.Practical implicationsThis study offers practical guidance for pharmaceutical companies, regulators, and third-party organisations to integrate artificial intelligence responsibly in pharmacovigilance. It highlights the role of leadership in delivering ethical AI adoption, shaping policy frameworks, and ensuring a balanced approach between technological innovation and human oversight in drug safety management.Social implicationsThis study has significant social implications, particularly in enhancing patient safety, improving public trust in drug monitoring systems, and addressing health disparities. Identified challenges such as data privacy concerns, algorithmic biases, and regulatory gaps must be addressed to prevent AI-driven inequities in healthcare.Originality/valueUnlike existing reviews that primarily focus on technological advancements or regulatory challenges, this research highlights the critical role of leadership in shaping ethical AI adoption and policy frameworks and balancing automation with human oversight. The findings will be valuable for policymakers, industry leaders, and regulators seeking to implement AI responsibly while maintaining trust and compliance in pharmaceutical safety management.