As artificial intelligence (AI) continues to transform clinical trials, ensuring transparency, accountability, and compliance has become critical. Central to achieving these goals is the implementation of a robust audit trail system. While the EU Artificial Intelligence Act (EU AI Act) does not explicitly define audit trail requirements for high-risk AI systems, it emphasizes the need for transparency, traceability, and risk management. In the context of clinical trials, this necessitates an advanced audit trail that bridges traditional compliance needs with modern AI oversight.
This blog explores what an ideal audit trail for AI in clinical trials should look like, combining traditional audit trail concepts with IT system logging practices to meet the stringent requirements of the EU AI Act.
The Role of Audit Trails in Clinical Trials
Traditionally, audit trails in clinical trials serve to:
- Document Data Changes: Tracking every modification to critical trial data, including who made the change, when, and why.
- Ensure Data Integrity: Protecting the accuracy, consistency, and reliability of clinical data.
- Support Regulatory Compliance: Providing evidence for regulatory audits and inspections (e.g., FDA, EMA).
- Enable Traceability: Ensuring transparency in biopharma clinical trial conduct and data management.
These functions are essential for maintaining trust in clinical research outcomes and for meeting compliance standards set by global health authorities.
Expanding Audit Trails for AI Systems
AI systems introduce new complexities that traditional audit trails are not equipped to handle. To comply with the EU AI Act and safeguard clinical trial integrity, audit trails must evolve to capture AI-specific activities and decisions.
Key Components of an Ideal AI Audit Trail
1. Comprehensive Data Lineage Tracking
- Detailed records of data sources, preprocessing steps, and transformations applied.
- Logs of how training, validation, and testing datasets were curated and utilized.
2. Model Development and Deployment Logs
- Version control for AI models, including training parameters, algorithms, and updates.
- Documentation of model training sessions, including datasets used and results.
3. Decision Traceability and Explainability
- Capturing the rationale behind AI-generated outputs, especially for patient selection and safety monitoring.
- Integration with Explainable AI (XAI) tools to provide interpretable insights into decision-making processes.
4. User Activity and Access Logs
- Detailed logs of user interactions with AI systems, including role-based access records.
- Monitoring of manual interventions in AI decision-making (aligning with Human-in-the-Loop requirements).
5. Risk and Bias Monitoring Records
- Logs of bias detection analyses, mitigation actions, and ongoing monitoring.
- Documentation of risk assessments and corrective actions throughout the AI lifecycle.
6. Incident Reporting and Resolution Logs
- Automated recording of system errors, performance deviations, and cybersecurity incidents.
- Corrective and preventive action (CAPA) documentation for issue resolution.
IT Considerations for Advanced Audit Trails
From an IT perspective, enabling an effective audit trail system for AI in clinical trials requires:
- Immutable Logging: Use of tamper-proof, cryptographically secured logs to prevent unauthorized changes.
- Real-Time Monitoring: Continuous system performance monitoring and anomaly detection.
- Automated Backups and Redundancies: Ensuring data durability and availability through secure storage.
- Scalability: Support for large, complex datasets and multiple AI models running simultaneously.
- Integration with Existing Systems: Seamless connection to clinical trial management systems (CTMS), electronic data capture (EDC) tools, and compliance platforms.
Benefits of a Robust AI Audit Trail
- Enhanced Compliance: Simplifies adherence to the EU AI Act’s transparency and accountability requirements.
- Increased Trust: Builds confidence among regulators, stakeholders, and patients through traceable AI operations.
- Improved Risk Management: Facilitates early detection of system biases, risks, and performance issues.
- Operational Efficiency: Streamlines regulatory reporting and audits with organized, accessible records.
Implementing an Effective AI Audit Trail Strategy
1. Define Audit Trail Objectives
- Align audit trail design with regulatory requirements, focusing on transparency, traceability, and risk management.
2. Leverage Scalable and Secure IT Solutions
- Adopt cloud-based or hybrid IT infrastructures with advanced logging, encryption, and backup capabilities.
3. Integrate Explainable AI Tools
- Use XAI solutions to generate interpretable insights and ensure decision-making transparency.
4. Conduct Regular System Audits and Reviews
- Implement continuous auditing processes to validate the performance and security of AI systems.
Conclusion
Developing a robust audit trail for AI systems in clinical trials is essential for regulatory compliance and ethical AI deployment. By expanding traditional audit trail practices to include AI-specific monitoring and documentation, biopharma companies can meet the EU AI Act’s transparency and accountability standards while safeguarding data integrity and patient safety.
Read our related blog: Data Governance Under the EU AI Act: From Clinical Trial Analytics to Compliance
Reach out to us today to find out how we can expedite your clinical development plan.
References
- European Union. (2024). Regulation (EU) 2024/1689 on Artificial Intelligence (AI Act). EUR-Lex
- European Commission. (2024). Artificial Intelligence Act: Overview and Key Provisions. European Commission Website
- Artificial Intelligence Act Developments. ArtificialIntelligenceAct.eu. artificialintelligenceact.eu
- Microsoft. (2025). Innovating in Line with the European Union’s AI Act. Microsoft On the Issues
