Guide to Using AI in Clinical Research Without Regulatory Headaches

Artificial Intelligence (AI) has transformative potential to accelerate and enhance clinical research productivity. Yet, regulatory uncertainties often make teams hesitant towards considering AI. This accessible guide simplifies the key regulations and provides clear guidance on confidently and responsibly integrating AI into clinical research.

Understanding the Regulations

Navigating the regulations that impact AI in clinical research can feel overwhelming. Here are three primary regulations to consider:

• General Data Protection Regulation (GDPR, EU 2018): GDPR ensures patient privacy through stringent rules on how personal data is processed, stored, and shared.
• EU AI Act (2024): The EU AI Act categorizes AI applications according to risk: minimal, limited, high-risk, and unacceptable. High-risk scenarios—such as autonomous patient diagnosis or recruitment decision-making without human oversight—face rigorous scrutiny. Conversely, lower-risk applications, such as logistical optimization, anonymized metric analysis, and clinical trial data exploration, have fewer regulatory barriers.
• FDA Guidance Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products (2025): The FDA's guidance clearly specifies that the guidance ‘does not address the use of AI models in drug discovery or when used for operational efficiencies (e.g., internal workflows, resource allocation, drafting/writing a regulatory submission) that do not impact patient safety, drug quality, or the reliability of results from a nonclinical or clinical study”. ?

What's Easily Achievable vs. What Requires Caution?

Not every AI application carries equal regulatory acceptance hurdles. Understanding what's generally acceptable versus what demands extra caution is key to confidently integrating AI. Here as easier way to navigate the applications of AI:

Rule of Thumb: AI can be used as an assisting tool to accelerate processes with human oversight rather than a standalone decision-maker to avoid regulatory compliance challenges.

AI Implementation Options: Cloud API vs. Local LLM

Selecting the right AI infrastructure from a regulatory compliance standpoint is crucial, while meeting your specific clinical research needs, and budget constraints. Here are your main options:

• Cloud-based APIs (e.g., Azure AI, OpenAI API): These are highly accessible and straightforward options with minimal upfront investment. Cloud APIs typically come with robust security measures and built-in compliance certifications. They excel in scenarios involving anonymized or encrypted data where rapid deployment and scalability are priorities. Potential considerations include understanding data residency and ensuring compliance with cross-border transfer rules, such as utilizing Standard Contractual Clauses (SCCs) for GDPR.
• Local Large Language Models (LLMs): Locally hosted AI models provide greater control over data security and compliance. These solutions are particularly suited to highly sensitive research where data cannot leave the premises or the country due to regulatory or organizational restrictions. However, local solutions often entail higher setup and operational costs, require dedicated infrastructure, and demand significant technical expertise for ongoing management and maintenance.

Quick tip: For general clinical research tasks that don’t involve direct patient-level decisions, cloud APIs provide a practical balance of simplicity, security, and compliance.

Practical Low Regulatory Concern Examples with Microsoft’s AI Infrastructure

Let's explore concrete ways in which we are effectively and compliantly utilizing Microsoft’s Azure AI infrastructure for clinical research scenarios with inherently low regulatory concerns. When using Azure AI, client data is never used to train or improve AI models. Microsoft explicitly states that user-submitted information remains confidential, segregated, and is processed solely for generating the user's outputs.

Azure’s infrastructure places a strong emphasis on security and privacy, with multiple layers of robust encryption applied both during data transmission (data in transit) and data storage (data at rest). This encryption ensures that data remains protected at all times, significantly simplifying compliance with stringent regulatory standards like GDPR. Azure also offers EU-based data centers, which support adherence to data residency regulations, providing additional reassurance that data never leaves the predefined geographical boundaries.

Additionally, when leveraging Azure AI, we utilize anonymized datasets or datasets explicitly stripped of Personal Identifiable Information (PII). For instance, we already have solutions or are actively working towards following use cases:

• Study EDC Setup: Planning database design, creating printable CRFs, developing edit checks, and preparing UAT plans directly derived from study protocols—all using fully anonymized data without patient-specific information.
• Planning Data Review Activities & identifying data issues: We're leveraging AI to enhance clinical data management, specifically in planning and identifying data quality issues. We are building solutions where AI assists in automatically drafting data review listings while flagging potential data issues, streamlining human oversight. E.g. by cross-referencing Adverse Events, Medical Histories, and Concomitant Medications data, AI can efficiently detect discrepancies, improving overall data quality and research integrity.
• Automated TFL Programming: Automatic scripting of Tables, Figures, and Listings (TFLs) from table-of-contents or predefined shells, without direct patient-level data exposure to AI.
• Automated SDTM Mapping: Streamlining complex mapping tasks from source data to SDTM standards with automated suggestions and corrections, significantly reduce manual programming effort.

In short, Azure AI tools greatly reduce compliance complexity by enforcing privacy-by-design principles, robust encryption, clear contractual safeguards, and stringent adherence to regulations like GDPR and the EU AI Act. This infrastructure allows researchers to confidently leverage AI capabilities without unnecessary exposure to regulatory uncertainty.

Conclusion: Empowered, Not Intimidated

Integrating AI into clinical research doesn’t need to be complicated or anxiety-inducing. Start with applications where the regulatory hurdles are low by involving anonymized data and routine research optimization tasks. Maintain consistent human oversight and lean on cloud AI solutions that come with built-in compliance features to confidently navigate regulatory frameworks.

AI is not your regulatory burden—it can be your partner to boost productivity.

At Nimble we are actively working towards easing these challenges for our customers, while assisting with AI risk assessments. Feel free to discuss your applications where AI can be applied.