Good AI Practice in Drug Development: Introduction

Artificial intelligence (AI) has the potential to transform how drugs (medicines) are developed and evaluated, ultimately improving health care. For Hong Kong healthcare professionals — clinicians involved in clinical trials, hospital pharmacists managing investigational products, and researchers in academia or industry — understanding how major regulators expect AI to be used in drug development is increasingly relevant as AI tools enter local research and regulatory workflows.

What this document covers

In January 2026, the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) jointly published Guiding Principles of Good AI Practice in Drug Development. This is an international harmonisation document — a shared foundation for regulators, standards organisations, and industry across jurisdictions.

In this context, AI refers to system-level technologies used to generate or analyse evidence across the drug product life cycle, including:

• Nonclinical research
• Clinical development and evaluation
• Post-marketing surveillance and pharmacovigilance
• Manufacturing processes

Why good practice matters for patient safety

Drugs are authorised based on demonstrated quality, efficacy, and safety, and when their benefits outweigh their risks. As new technologies emerge, including AI, it is essential that their use reinforces these requirements for the benefit and safety of patients.

The use of AI throughout the drug product life cycle has increased significantly in recent years. The complex and dynamic processes involved in developing, deploying, using, and maintaining AI technologies benefit from careful management throughout the life cycle to ensure outputs are accurate and reliable.

Expected benefits — and the need for governance

Among other innovations, AI technologies are anticipated to support a multifaceted approach that:

• Promotes innovation
• Reduces time-to-market
• Strengthens regulatory excellence and pharmacovigilance
• Decreases reliance on animal testing by improving prediction of toxicity and efficacy in humans

These 10 guiding principles lay the foundation for developing good practice that addresses the unique nature of AI technologies and helps cultivate future growth in this rapidly progressing field.

International collaboration beyond FDA and EMA

The principles identify areas where international regulators, standards organisations, and collaborative bodies could work to advance good practice. Areas of collaboration include:

• Research
• Creating educational tools and resources
• International harmonisation
• Consensus standards that may inform regulatory policies and guidelines in different jurisdictions

As the use of AI in drug development evolves, so too must good practice and consensus standards. Strong partnerships with international public health partners will be crucial to empower stakeholders to advance responsible innovations.

For Hong Kong professionals

While Hong Kong has its own regulatory framework, harmonised FDA–EMA principles provide a shared reference when evaluating AI used in clinical research, pharmacovigilance submissions, or manufacturing quality systems — especially where products target US or EU markets.

The following articles break down all ten principles in plain language, grouped for practical learning.

Source: FDA & EMA — Guiding Principles of Good AI Practice in Drug Development (January 2026)