EMA and FDA Establish 10 Guiding Principles for the Responsible Use of Artificial Intelligence in Drug Development

Artificial intelligence (AI) is rapidly transforming the pharmaceutical and life sciences industries. From early research and clinical development to manufacturing and safety monitoring, AI is becoming increasingly relevant to how scientific evidence is generated, evaluated, and managed.

In January 2026, the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA) jointly published the 10 Guiding Principles of Good AI Practice in Drug Development.

The principles are intended to promote the responsible use of AI throughout the medicine lifecycle. They provide a common foundation for pharmaceutical companies, biotechnology organizations, research institutions, technology providers, and other stakeholders developing or using AI-based solutions.

The agencies emphasize that AI should be implemented with appropriate human oversight, scientific rigor, data governance, transparency, and risk-based controls. The principles apply to evidence generation and monitoring across areas ranging from early research and clinical trials to manufacturing and pharmacovigilance.

The 10 Guiding Principles

1. Human-Centric by Design

AI systems should be designed to support and complement human expertise.

Qualified professionals must remain involved in important scientific, quality, clinical, and regulatory decisions. AI should strengthen human decision-making rather than remove accountability or replace professional judgment.

2. Risk-Based Approach

The level of control, documentation, validation, and oversight should be proportional to the risks associated with the AI application.

An AI system used for a low-impact administrative activity will not necessarily require the same controls as a system whose output may influence product quality, patient safety, clinical decisions, or regulatory submissions.

3. Adherence to Standards

AI applications should be developed and used in accordance with applicable legal, ethical, technical, scientific, cybersecurity, data-protection, and quality standards.

In regulated environments, organizations must also evaluate how the AI system interacts with existing pharmaceutical quality systems and relevant GxP requirements.

4. Clear Context of Use

Every AI system should have a clearly defined purpose and context of use.

Organizations should document what the system is designed to do, who will use it, what data it requires, where it will operate, and what its limitations are.

Clearly defining the context of use is essential for determining the appropriate development, validation, monitoring, and governance strategy.

5. Multidisciplinary Expertise

The implementation of AI in drug development requires collaboration among professionals from different areas. Depending on the application, this may include:

• Pharmaceutical and clinical scientists
• Quality and validation professionals
• Regulatory specialists
• Data scientists
• Software engineers
• Cybersecurity professionals
• Statisticians
• Subject-matter experts

A multidisciplinary approach helps ensure that both the technical performance and the regulated use of the system are properly evaluated.

6. Data Governance and Documentation

The reliability of an AI system depends heavily on the quality, relevance, integrity, and representativeness of its data. Organizations should establish appropriate controls for:

• Data provenance
• Data quality
• Traceability
• Security and privacy
• Access management
• Documentation
• Data retention
• Bias identification and mitigation

Documentation should be maintained throughout the system lifecycle to support transparency, reproducibility, and regulatory evaluation.

7. Model Design and Development Practices

AI models should be developed using structured and scientifically sound practices.

This includes defining the model's intended purpose, selecting appropriate datasets, documenting assumptions, managing versions, testing the system, evaluating limitations, and maintaining reproducibility.

Development decisions should be justified and documented according to the risk and importance of the AI application.

8. Risk-Based Performance Assessment

The performance of an AI system should be evaluated using methods and metrics that are appropriate for its intended context of use. Testing should consider not only general accuracy but also factors such as:

• Reliability
• Robustness
• Generalizability
• Potential bias
• Data variability
• Failure conditions
• Impact on users and regulated processes

The level of performance assessment should be proportional to the potential consequences of an incorrect or unreliable output.

9. Lifecycle Management

AI systems should not be considered complete after their initial implementation. Their performance may change as datasets, software environments, business processes, user behavior, or scientific knowledge evolve.

Organizations should therefore establish procedures for:

• Ongoing performance monitoring
• Change control
• Periodic review
• Version management
• Model updates
• Deviation management
• Revalidation when appropriate
• Retirement or replacement of the system

This lifecycle approach is particularly important for AI models that may continue to evolve after deployment.

10. Clear, Essential Information

Organizations should maintain clear and sufficient information about the AI system and communicate it to the appropriate stakeholders. The available information should allow users, auditors, quality teams, and regulatory authorities to understand:

• The system's intended use
• Its principal characteristics
• The data used
• Its performance
• Its limitations
• The controls applied
• How risks are managed
• How changes are monitored

Transparency is fundamental to establishing confidence in AI-supported evidence and decisions.

What These Principles Mean for the Pharmaceutical Industry

The joint initiative from EMA and FDA sends a clear message: artificial intelligence is expected to become increasingly important in medicine development, but its adoption must be supported by appropriate governance and scientific control.

The principles do not replace existing regulations. Instead, they provide a shared framework that helps organizations evaluate how AI should be designed, implemented, assessed, and maintained within regulated activities.

For pharmaceutical manufacturers, biotechnology companies, contract laboratories, research organizations, and technology providers, responsible AI adoption requires more than implementing advanced software. Organizations will need to demonstrate:

• A clearly defined intended use
• Appropriate risk assessment
• Reliable and well-governed data
• Transparent documentation
• Human oversight
• Effective lifecycle management

The ability to explain and document these elements will be essential for building trust among users, customers, auditors, and regulatory authorities.

Human Oversight Remains Essential

One of the most significant aspects of the EMA and FDA guidance is its emphasis on human-centered AI.

Artificial intelligence can process large volumes of information, identify patterns, support scientific analysis, and improve access to organizational knowledge. However, it should never eliminate the responsibility of qualified professionals.

In regulated life sciences environments, final decisions must remain with individuals who understand the scientific context, regulatory requirements, and potential consequences of those decisions.

AI should therefore be positioned as a decision-support technology, not as an autonomous replacement for scientific, quality, clinical, or regulatory judgment.

Data Integrity and Traceability

The value of an AI system depends directly on the quality of the information it uses.

Incomplete, outdated, poorly controlled, or unrepresentative data may generate unreliable outputs, regardless of how sophisticated the underlying technology is.

Organizations should therefore ensure robust governance throughout the entire data lifecycle.

Validation and Continuous Monitoring

Traditional computerized systems are generally validated against predefined requirements. AI systems introduce additional complexity because their performance may depend on evolving data, operating environments, and user interactions.

A risk-based validation strategy should therefore evaluate the complete AI-enabled system rather than focusing exclusively on the algorithm.

Validation should be viewed as a continuous process rather than a one-time event.

The Future of AI in Regulated Life Sciences

The publication of common principles by EMA and FDA represents an important milestone toward global regulatory alignment.

It demonstrates that regulatory agencies recognize AI as a strategic technology capable of accelerating innovation across the medicine lifecycle, provided its implementation remains transparent, scientifically justified, and properly governed.

Organizations that invest today in robust AI governance frameworks will be better prepared for future regulatory expectations.

Advanced Life Sciences' Perspective

At Advanced Life Sciences, we believe that artificial intelligence should strengthen scientific, operational, and regulatory excellence while preserving human responsibility.

This perspective is reflected in the ongoing development of Pharma Intelligence, our enterprise platform designed to support knowledge management, compliance activities, validation processes, controlled documentation, asset management, training, and intelligent decision support for regulated life sciences organizations.

Our objective is not to replace professionals or make autonomous regulated decisions. Instead, Pharma Intelligence is being developed to provide a structured environment where information can be securely organized, connected, reviewed, and accessed with greater efficiency, traceability, and compliance.

As regulatory expectations continue to evolve, transparency, governance, documentation, and human oversight will remain fundamental principles for the responsible implementation of AI.

The future of artificial intelligence in life sciences will not be defined only by what the technology is capable of doing. It will also be defined by how safely, transparently, and responsibly organizations choose to use it.

References

• European Medicines Agency (EMA). EMA and FDA set common principles for AI in medicine development. European Medicines Agency, January 14, 2026.
• European Medicines Agency (EMA). Guiding Principles of Good AI Practice in Drug Development. European Medicines Agency, 2026.
• U.S. Food and Drug Administration (FDA). Guiding Principles of Good AI Practice in Drug Development. U.S. Food and Drug Administration, January 14, 2026.