Artificial intelligence is no longer a future-state concept in drug development—it is actively shaping how therapies are discovered, tested, manufactured, and monitored. From accelerating target identification to optimizing clinical trial design and strengthening post-market safety surveillance, AI systems are increasingly embedded across the pharmaceutical value chain.
Regulators are paying close attention. In 2026, the FDA, in collaboration with the European Medicines Agency (EMA), released its Good AI Practice (G-AI-P) principles to provide a structured framework for responsible AI use in drug development. The message is clear: AI holds great potential, but the potential for utilizing it must move in lockstep with quality, safety, and regulatory rigor to ensure patient safety and regulatory excellence.
Here’s what industry stakeholders need to know.
AI’s integration into regulated workflows has accelerated rapidly. Sponsors are using AI to:
These systems increasingly influence decisions that directly impact patient safety and product quality. The FDA recognizes AI’s potential to improve decision-making, reduce development timelines, and enhance outcomes, but also understands that poorly governed AI could introduce new risks.
The Good AI Practice principles are designed to ensure that AI strengthens, rather than undermines, regulatory standards.
What the Guidance Covers
The FDA’s framework applies across the entire drug product lifecycle, including:
Importantly, this guidance is not limited to experimental AI tools. It applies to AI systems used to generate, analyze, or support regulatory evidence and decision-making. In other words, if AI meaningfully influences data submitted to regulators, or decisions that affect product quality or patient safety, it falls within the preexisting scope.
The FDA emphasizes that AI must reinforce, not replace, existing standards for safety, efficacy, and quality. Essentially, AI outputs must be:
The agency also acknowledges that AI technologies evolve quickly. Building trust will require strong public-private collaboration, harmonized standards, and adaptive oversight mechanisms.
At the center of this framework are ten guiding principles.
1. Human-Centric by Design
AI systems should align with ethical values and prioritize patient benefit. Human oversight remains essential.
AI should be utilized as a decision-support tool, not an autonomous replacement for scientific or clinical judgment.
2. Risk-Based Approach
Oversight and validation should scale according to risk.
AI used in low-impact contexts may require lighter controls. Systems influencing high-stakes clinical or manufacturing decisions demand more rigorous validation and governance. This principle mirrors long-standing regulatory approaches to risk management.
3. Adherence to Standards
AI systems must adhere to:
AI should not operate outside established compliance frameworks. Instead, it must integrate into them.
4. Clear Context of Use
Ambiguity increases regulatory risk. To minimize this risk, every AI system must have a clearly defined role and scope for use. Sponsors must articulate:
5. Multidisciplinary Expertise
Successful implementation of AI requires broad expertise covering both the AI technology and its context of use. Throughout the technology’s lifecycle, this necessitates the integration of:
6. Data Governance & Documentation
Data is foundational to AI performance. The FDA stresses that all information about where data comes from, how it's processed, and the choices made during analysis are thoroughly recorded, making it traceable and verifiable according to GxP standards.
Throughout the entire life cycle of the technology, proper governance must also be upheld, ensuring privacy and safeguarding sensitive information.
7. Model Design & Development Best Practices
Developing strong models and systems enhances the transparency, reliability, generalizability, and robustness of AI technologies, which in turn supports patient safety. The agency highlights several technical expectations:
Transparency is not optional. Regulators must be able to understand how a system reaches its outputs, particularly when those outputs affect safety or efficacy.
8. Risk-Based Performance Assessment
Performance evaluation must reflect the complete system, including human-AI interaction. Testing metrics should align with the defined context of use. Validation must assess real-world performance, not just theoretical accuracy.
9. Lifecycle Management
AI systems are dynamic. Models may drift as data environments evolve, and it’s important to keep this in mind.
The FDA calls for:
Validation is not a one-time event. It is a continuous responsibility.
10. Clear, Essential Information
AI transparency extends beyond regulators. Communication must be understandable to:
Plain-language explanations should cover:
Clarity is a foundational principle. It builds trust and reduces misuse.
The FDA’s Good AI Practice principles signal a clear direction: AI adoption in drug development must be accompanied by governance, documentation, accountability, and transparency.
These principles are likely to serve as the foundation for future, more detailed guidance and continued international harmonization.
For companies investing in AI, the takeaway is strategic:
Organizations that proactively integrate these principles into their AI strategies will not only reduce friction in regulatory environments. They will strengthen stakeholder confidence and accelerate responsible innovation.
AI is reshaping drug development. The FDA’s framework ensures it does so safely.