AI in quality # FDA's seven-step credibility framework for AI, in plain terms By QLogix · Published 12 October 2026 · 4 min read FDA's draft guidance asks sponsors to show that an AI model is credible for a specific context of use. The effort scales with model risk: how much the model's output drives the decision, and how serious a wrong decision would be. Steps in the credibility assessment framework. 7 Drug and biologic submissions with AI components FDA has seen since 2016. 500+ Factors that set model risk: model influence and decision consequence. 2 ## Key takeaways - FDA published the draft guidance in January 2025. It was still a draft when this article was last updated. - It applies when an AI model produces information used to support a regulatory decision about the safety, effectiveness or quality of a drug or biological product, including in manufacturing. - Credibility is trust in the model's performance for one context of use, supported by evidence. It is not a property of the model in general. - Model risk combines two things: how much the output influences the decision, and the consequence if the decision is wrong. ## What is the FDA draft guidance on AI for drugs and biologics? In January 2025 FDA published the draft guidance Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. It gives sponsors a risk-based framework for establishing and assessing the credibility of an AI model for a particular context of use. The comment period closed in April 2025. A draft guidance describes FDA's current thinking and is not binding. It is still the clearest statement of what the agency expects to see when AI output supports a decision it will review. ## When does it apply? It applies to AI models that produce information or data intended to support regulatory decisions about the safety, effectiveness or quality of drugs and biological products. That reaches across the product life cycle: nonclinical work, clinical trials, postmarketing surveillance and manufacturing. It does not address AI used in drug discovery, or AI used for operational efficiency, such as drafting or resource planning, where the use does not affect patient safety, drug quality or the reliability of study results. ## What are the seven steps? - Step 1. Define the question of interest: the specific question or decision the model will address. - Step 2. Define the context of use: what the model does, what it is used for, and whether other evidence is used alongside its output. - Step 3. Assess the AI model risk, from model influence and decision consequence. - Step 4. Develop a plan to establish the credibility of the model output within the context of use. - Step 5. Execute the plan. - Step 6. Document the results of the credibility assessment plan and discuss deviations from it. - Step 7. Determine whether the model is adequate for the context of use. The seven-step credibility assessment - 1 Question of interest The decision the model informs - 2 Context of use The model's role and scope - 3 Model risk Influence and consequence - 4 Credibility plan How the output will be shown to be trustworthy - 5 Execute Run the plan - 6 Document Results and deviations - 7 Adequacy Fit for the context of use, or not Steps 1 to 3 decide how much evidence is needed. Steps 4 to 7 produce it and judge it. ## How is AI model risk assessed? Model risk is not the risk of the model in the abstract. It is the risk that its output leads to a wrong decision with an adverse outcome. The draft builds it from two factors. - Model influence: how much the AI output contributes to the decision, compared with other evidence. A model whose output is confirmed by an independent test has less influence than one that decides alone. - Decision consequence: how serious the outcome is if the decision turns out to be wrong. How the two factors combine into model risk Model influence Low Medium High High Medium High High Medium Low Medium High Low Low Low Medium Decision consequence An illustration of the draft guidance's two factors. The draft does not prescribe a scoring grid; sponsors justify the risk level for their own context of use. ## What does this look like in manufacturing and quality? Take a model that screens batch data and flags batches for closer review. If every batch still goes through the full release testing and a qualified person makes the release decision, the model's influence is low: it directs attention and decides nothing. If the same model were used to skip testing on batches it scores as normal, its influence would be high, and so would the evidence FDA expects. The practical lesson is that design choices move the risk. Keeping a qualified reviewer and an independent check in the process lowers model influence, and that lowers the validation burden along with the risk to the patient. ## What should you document? - The question of interest and the context of use, written precisely enough that a reviewer can tell what is in and out of scope. - The model and how it was developed: the data used for training and tuning, and how it was managed. - How the model was evaluated: test data independent of development data, the performance metrics and the acceptance criteria. - Life cycle maintenance: how performance is monitored after deployment and how changes to the model are controlled. ## Common questions ### Is the FDA AI guidance for drugs and biologics final? It was still a draft when this article was last updated in October 2026. FDA published it in January 2025 and the comment period closed in April 2025. ### What is a context of use? The context of use defines the specific role and scope of the AI model used to address a question of interest: what is modelled, how the output is used, and whether other information is used with it to answer the question. ### Does the FDA draft guidance cover AI that drafts documents? Not where the use is only for operational efficiency and does not affect patient safety, drug quality or the reliability of study results. The draft sets those uses outside its scope. Your own quality system still decides how such tools are controlled. ## Sources - FDA draft guidance: Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products (January 2025) - FDA press announcement, 6 January 2025: FDA proposes framework to advance credibility of AI models used for drug and biological product submissions This article is general information for quality and regulatory professionals. It is not regulatory or legal advice. Check the source documents for their current status before acting on them. ## Topics - FDA - AI credibility - Context of use - Drug manufacturing ## How QLogix helps - Connected quality data - Validation of AI-enabled systems All articles ## More from the blog - Manufacturing science · 12 October 2026AI in drug manufacturing: where FDA stands and what to build forFDA has no final guidance written only for AI in drug manufacturing. It has a 2023 discussion paper naming five open areas and a 2025 draft framework that covers manufacturing models. Together they show what a process model has to prove. - Medical devices · 12 October 2026AI-enabled medical devices: what FDA's list and guidance showFDA's public list counted 1,614 AI-enabled medical devices through June 2026, about three in four of them in radiology. Two documents shape how they are built and changed: the final guidance on predetermined change control plans and the draft guidance on lifecycle management. - Manufacturing science · 12 October 2026Robotics in aseptic manufacturing: what Annex 1 expectsThe revised EU GMP Annex 1 names robotic systems, with isolators and RABS, as technologies to consider for protecting sterile products. Taking the operator out of the critical zone removes the largest contamination source and moves the quality work to design, software and data. ## Talk it through with someone who has run a plant Bring your backlog, your systems map or one investigation that keeps coming back. We will tell you plainly what we would do and what we would leave alone. Request a meeting Email us In the first meeting we cover - Your current QMS and how it connects to MES, LIMS and ERP - Where deviations, CAPAs and change controls slow down - Whether a readiness assessment makes sense, and what it would cover --- Source: https://qlogix.ai/blog/fda-ai-credibility-framework QLogix · contact@qlogix.ai · +17146639012