From discovery to delivery: Governance of AI in the pharmaceutical industry

Abstract

• AI boosts efficiency and accuracy in drug discovery and development. • Data privacy, bias, and regulatory and ethical concerns warrant AI governance. • Governance promotes ethical AI use, regulatory compliance, trust among stakeholders. • Transparency, accountability, and data governance yield fair and accurate solutions. • Continuous monitoring and evaluation ensure continued safety and compliance. Artificial Intelligence (AI) is revolutionizing the pharmaceutical industry, significantly enhancing drug discovery, patient care, and operational efficiency. Key AI technologies like machine learning, deep learning, natural language processing, and computer vision are transforming pharmaceutical practices. Despite the promising potential, AI implementation faces numerous challenges such as technical complexity, ethical concerns, regulatory hurdles, and a shortage of skilled professionals. Governance frameworks are essential to ensure AI technologies are ethically developed and deployed, balancing innovation with safety and transparency. Key components of AI governance include regulatory compliance, data governance, algorithm transparency, and continuous system monitoring. However, the fast pace of technological advancements, global regulatory discrepancies, and the need for stakeholder collaboration present ongoing challenges. Best practices for AI governance, such as promoting transparency, fostering multidisciplinary collaboration, and adhering to robust data management standards, are critical for ensuring the ethical and effective use of AI. Addressing these challenges will enable the pharmaceutical industry to fully harness the power of AI, ensuring patient safety and promoting innovation in healthcare.

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