Report on the Responsible Implementation of Artificial Intelligence in Pharmacovigilance in Alignment with Sustainable Development Goals

Executive Summary

This report outlines a strategic framework for the responsible implementation of Artificial Intelligence (AI) in pharmaceutical drug safety operations. It analyzes the current regulatory environment and key challenges, emphasizing the critical role of robust AI governance. The adoption of AI in pharmacovigilance is presented as a vital contributor to achieving several United Nations Sustainable Development Goals (SDGs), particularly those concerning health, innovation, and institutional strength.

Advancing Global Health and Well-being (SDG 3)

The primary objective of integrating AI into pharmacovigilance is to enhance patient safety, a core target of SDG 3: Good Health and Well-being. By improving the efficiency and accuracy of adverse event detection and analysis, AI systems can significantly strengthen public health outcomes.

• Ensuring transparency in AI operations builds patient and regulatory trust, which is fundamental to effective healthcare systems.
• Proactive AI governance directly supports the goal of ensuring healthy lives by minimizing drug-related harm.
• Future-proofing pharmacovigilance operations ensures sustained protection for global populations against potential medication risks.

Fostering Innovation and Resilient Infrastructure (SDG 9)

The adoption of generative AI represents a significant technological advancement for the pharmaceutical sector, aligning with SDG 9: Industry, Innovation, and Infrastructure. Building a governance framework for this technology is essential for creating resilient, sustainable, and inclusive industrialization.

Key challenges in this domain include:

1. Navigating the evolving global regulatory landscape for AI technologies.
2. Overcoming the complexities associated with the adoption of advanced generative AI models.
3. Establishing clear metrics for compliance and operational transparency to build resilient systems.

Building Strong Institutions and Partnerships (SDG 16 & SDG 17)

A robust AI governance strategy is crucial for building effective and accountable systems, a cornerstone of SDG 16: Peace, Justice, and Strong Institutions. Compliance with international regulations ensures that pharmaceutical companies operate as responsible and transparent institutional actors.

Furthermore, successful implementation relies on collaboration, directly supporting SDG 17: Partnerships for the Goals.

• The selection of qualified and ethical technology partners is a critical step in forming strategic partnerships for sustainable development.
• Building trust in AI systems requires a multi-stakeholder approach involving regulators, technology providers, and healthcare professionals.
• Proactive governance strategies create a foundation for long-term partnerships aimed at achieving shared public health and innovation goals.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 3: Good Health and Well-being

  The article’s central theme is improving “drug safety” and “patient safety” through AI in pharmacovigilance. This directly contributes to ensuring healthy lives and promoting well-being by minimizing the harm caused by medicines.

• SDG 9: Industry, Innovation, and Infrastructure

  The text focuses on the pharmaceutical industry’s adoption of advanced technology (“generative AI”) to innovate and “future-proof” its operations. This aligns with upgrading industrial capabilities and fostering innovation.

• SDG 16: Peace, Justice, and Strong Institutions

  The article emphasizes the need for “AI governance,” “transparency,” and “compliance” with “global regulations.” This relates to building effective, accountable, and transparent institutional practices within the pharmaceutical sector.

• SDG 17: Partnerships for the Goals

  The mention of the need to “select the right technology partners” highlights the importance of collaboration between pharmaceutical companies and technology providers to achieve the goal of responsible AI implementation.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. SDG 3: Good Health and Well-being

   • Target 3.8: Achieve universal health coverage, including access to safe, effective, quality and affordable essential medicines. The article’s focus on enhancing “drug safety” and “patient safety” directly supports the goal of ensuring medicines are safe and effective for all.

2. SDG 9: Industry, Innovation, and Infrastructure

   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors…encouraging innovation. The article explicitly discusses how pharmaceutical companies can “responsibly implement AI” and adopt “generative AI,” which is a direct example of upgrading technological capabilities and encouraging innovation within the industry.

3. SDG 16: Peace, Justice, and Strong Institutions

   • Target 16.6: Develop effective, accountable and transparent institutions at all levels. The article’s call for “AI governance,” “transparency,” and “compliance” is about creating accountable and transparent systems within pharmaceutical companies (institutions) for managing drug safety.

4. SDG 17: Partnerships for the Goals

   • Target 17.17: Encourage and promote effective public, public-private and civil society partnerships. The advice to “select the right technology partners” points directly to the need for private-private partnerships to leverage expertise and resources for successful AI implementation.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

The article does not mention official UN indicators, but it implies several qualitative and quantitative measures for progress:

• Indicator for Patient Safety (related to Target 3.8): The core goal of ensuring “patient safety” implies a need to measure outcomes such as the rate of detection of adverse drug reactions or a reduction in medication-related harm.
• Indicator for Governance and Compliance (related to Target 16.6): The emphasis on “AI governance” and “compliance” implies the development and implementation of governance frameworks. Progress could be measured by the number of companies with such frameworks in place or by tracking adherence to “evolving global regulations.”
• Indicator for Trust (related to Target 16.6): The objective to “build trust in AI systems” implies a need for indicators that measure the confidence of stakeholders (regulators, healthcare professionals, patients) in these new technologies.
• Indicator for Technology Adoption (related to Target 9.5): The discussion on “adopting generative AI” implies that progress can be measured by the rate of adoption of these advanced technologies within pharmacovigilance operations across the pharmaceutical industry.

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: iqvia.com