The FDA’s latest draft guidance on artificial intelligence (AI) is setting the stage for how AI can be used to support regulatory decision-making in drug and biological product development. This is a significant step forward, offering clarity on how AI models should be evaluated for safety, effectiveness, and quality. At Advantage Clinical, we’re always monitoring regulatory shifts to help our partners stay ahead of compliance challenges and maximize innovation. Here’s what you need to know about this new guidance and its impact on clinical research.
Why This FDA AI Guidance Matters
AI has been increasingly integrated into the drug product life cycle, from clinical trials to pharmacovigilance and manufacturing. However, without proper oversight, AI models can introduce risks related to data reliability, bias, and transparency. The FDA’s guidance is designed to ensure that AI is used responsibly and effectively to support regulatory decisions.
This guidance applies to AI models that generate data or insights to support regulatory submissions, but it does not cover AI’s use in drug discovery or administrative efficiencies such as streamlining internal workflows or document drafting.
The FDA’s 7-Step AI Credibility Framework
To establish the credibility of AI models, the FDA recommends a risk-based assessment. Here’s a simplified breakdown of the 7-step framework:
- Define the Question of Interest
Clearly identify the regulatory question the AI model aims to address. For example, is it being used to predict patient outcomes or assess drug manufacturing quality?
- Define the Context of Use (COU)
Specify how the AI model will be applied, including whether additional human oversight or complementary data will be used alongside AI-generated insights.
- Assess AI Model Risk
FDA evaluates model risk based on:
- Model Influence – How much weight does the AI model carry in the final decision?
- Decision Consequence – What’s the impact of an incorrect AI-driven decision?
A high-risk AI model, such as one determining patient eligibility for monitoring in a clinical trial, will require greater validation and oversight compared to a lower-risk model (e.g an AI tool assisting with data visualization).
- Develop a Plan to Establish AI Model Credibility
The plan should include:
- A detailed model description (features, algorithms, training process)
- Information on data sources used to train the model
- Testing & evaluation metrics to measure model performance
- Execute the Credibility Plan
Once the credibility plan is designed, it must be implemented through validation studies, testing, and documentation.
- Document Results & Deviations
Any changes made to the AI model during testing should be documented to ensure transparency and regulatory alignment.
- Determine Model Adequacy
If the AI model does not meet credibility expectations, sponsors may need to:
- Adjust model influence (e.g., use it as a supportive tool rather than a primary decision-maker)
- Improve training data to reduce bias
- Enhance model transparency
AI Model Life Cycle: Ensuring Ongoing Compliance
One of the biggest challenges in AI adoption is model evolution. The FDA emphasizes the need for continuous monitoring to ensure AI models remain valid over time. This is especially critical for AI tools used in pharmaceutical manufacturing and post-marketing surveillance, where input data can shift, affecting model accuracy.
Sponsors should plan for regular AI audits, updates, and revalidation as part of their regulatory compliance strategy.
The FDA strongly encourages early interactions with sponsors to discuss AI model credibility. Various engagement pathways are available, including:
- Complex Innovative Trial Design (CID) Program for AI-driven clinical trials
- Real-World Evidence (RWE) Program for AI-supported observational studies
- Emerging Drug Safety Technology Program (EDSTP) for AI in pharmacovigilance
Early engagement helps sponsors align their AI strategies with regulatory expectations, reducing the risk of delays in drug approval.
What This May Mean for Your Clinical Research Program
For pharmaceutical companies, CROs, and clinical research teams, this guidance highlights the need for rigorous AI model validation before submission to regulatory authorities. AI can be a powerful tool, but it must be implemented with clear documentation, transparency, and ongoing monitoring.
The FDA’s approach to AI is still evolving, and we expect further refinements as technology advances. Stay informed by following our blog, subscribing to regulatory updates, and partnering with industry experts who can help you navigate AI compliance with confidence.
To read the full FDA Draft Guidance on AI, visit: FDA AI Guidance Document.
