Artificial intelligence is now positioned by FDA as a regulatory science topic spanning essentially the entire lifecycle of regulated medical products.
In its current Focus Areas of Regulatory Science framework, FDA identifies artificial intelligence as relevant to:
- product development;
- commercial manufacturing;
- regulatory assessment;
- postmarket surveillance;
- inspectional activities.
For pharmaceutical companies, one point is particularly important.
FDA explicitly states that it is:
exploring how AI is used during commercial drug manufacturing to improve quality decision-making.
This places AI directly within the pharmaceutical-quality and manufacturing regulatory-science agenda rather than treating it solely as a clinical, digital-health or administrative technology.
AI Is Being Considered Across the Entire Product Lifecycle
FDA’s regulatory science framework shows AI as applicable across:
- product characterisation, manufacturing and quality;
- nonclinical premarket evaluation;
- clinical premarket evaluation;
- postmarket activities.
This broad scope matters.
Pharmaceutical AI governance is sometimes organised as if different applications belong to completely separate domains:
R&D AI
Clinical AI
Manufacturing AI
Quality AI
Regulatory AI
FDA’s regulatory-science perspective increasingly suggests that these should instead be seen as different Contexts of Use within one broader AI lifecycle.
The technology may differ, but recurring regulatory questions remain similar:
- Are the data appropriate?
- Is the model sufficiently reliable?
- Is performance robust to changing conditions?
- Can the output be understood and verified?
- How are changes controlled?
- Who remains accountable?
Commercial Drug Manufacturing Is Explicitly Included
The direct reference to commercial drug manufacturing is particularly relevant for GMP professionals.
Potential AI applications in manufacturing can include:
- process monitoring;
- predictive process models;
- advanced process control;
- deviation and trend analysis;
- predictive maintenance;
- visual inspection;
- Quality System analytics;
- knowledge management.
FDA does not state that all of these uses are automatically acceptable.
The important signal is that the Agency is actively studying how AI can support manufacturing quality decisions.
This means that AI in manufacturing is increasingly becoming a regulatory-science problem rather than merely an industry innovation project.
Robustness to Changing Conditions Is Becoming a Regulatory Question
Another particularly important FDA research activity concerns model robustness.
FDA describes work aimed at understanding how machine-learning models respond to:
- changes in real-world data;
- changing contextual factors;
- distributional shifts;
- different intended-use conditions.
This issue is fundamental for pharmaceutical AI.
A model may perform well during development and validation but later encounter:
- different raw-material characteristics;
- new equipment;
- process changes;
- new sites;
- different populations;
- changing documentation patterns;
- different operating conditions.
The key regulatory question therefore becomes:
Does the model remain sufficiently reliable when its operating environment changes?
This is closely related to the concept of maintaining AI in a continued state of control.
Validation Cannot Stop at Initial Accuracy
For conventional software, validation commonly demonstrates that predefined functions operate as expected.
AI requires an additional question:
Will the model continue to perform adequately when the data distribution or context changes?
This can require controls such as:
- ongoing performance monitoring;
- drift detection;
- periodic reassessment;
- defined performance limits;
- change-control triggers;
- revalidation criteria.
A possible lifecycle therefore becomes:
Initial Validation
↓
Controlled Deployment
↓
Operational Performance Monitoring
↓
Detection of Context or Data Change
↓
Impact Assessment
↓
Revalidation or Model Update Where Necessary
FDA Is Also Studying Synthetic Data
FDA’s current AI research activities include evaluating synthetic datasets used for model training and testing.
This is another important area for pharmaceutical companies.
Synthetic data may potentially support:
- model development where real data are limited;
- privacy protection;
- simulation of rare events;
- test-set expansion;
- model robustness assessment.
But synthetic data create their own credibility questions.
For example:
- How representative are they?
- What assumptions were used to generate them?
- Could important edge cases be missing?
- Could bias in the source data be amplified?
- Can the generation process be reconstructed?
Synthetic data therefore do not eliminate the need for data governance.
They create an additional data-generation process that itself may need to be understood and controlled.
AI Is Also Entering Regulatory Review and Pharmacovigilance
FDA describes several internal or research applications involving AI.
These include:
- natural-language processing of regulatory submissions;
- classification of submission complexity;
- analysis of clinical-trial data;
- processing and evaluation of safety reports;
- MedDRA coding support;
- pharmacometrics;
- precision-medicine applications.
This is strategically important for pharmaceutical companies.
AI is increasingly appearing on both sides of the regulatory interface.
Companies may use AI to:
generate → analyse → organise → submit regulatory information
while regulators may use AI to:
retrieve → classify → analyse → review regulatory information.
This increases the value of structured, traceable and machine-readable data.
FDA Is Building AI Knowledge Among Inspectors
One of the most interesting points from a GMP perspective is that FDA describes workshops designed to introduce AI and machine learning to Agency personnel.
FDA specifically notes that this helps keep its inspectional cadre informed about how these technologies may affect investigations.
This is a meaningful regulatory signal.
AI governance is gradually becoming relevant not only to:
- regulatory policy;
- scientific review;
- guidance development;
but also to:
inspection capability.
For pharmaceutical companies, this means AI could increasingly become part of ordinary GMP inspection discussions whenever it materially affects a regulated process.
What Could an Inspector Eventually Ask?
A future inspection involving an AI-enabled quality or manufacturing process could reasonably explore questions such as:
- Where is AI used?
- What is the intended use?
- What GMP decision does it influence?
- Which data are used?
- How was performance evaluated?
- What are the known failure modes?
- How are model and configuration changes controlled?
- How is ongoing performance monitored?
- What happens if the AI becomes unreliable?
- Who is responsible for the final quality decision?
These are not presented by FDA as a new inspection checklist.
They are logical consequences of treating AI as part of the regulated pharmaceutical process.
A Broader AI Assurance Model Is Emerging
FDA’s regulatory-science priorities reinforce a pattern increasingly visible across pharmaceutical AI regulation.
The assurance model is moving beyond:
Validate the model.
towards:
Intended Use → Data → Performance → Robustness → Human Oversight → Change Control → Monitoring → Accountability
This is significantly closer to lifecycle Quality Risk Management than to conventional one-time software testing.
Why This Is Important
The significance of FDA’s current regulatory-science position is that AI is no longer treated as a narrow emerging technology.
It is explicitly connected with:
- commercial drug manufacturing;
- quality decision-making;
- regulatory assessment;
- postmarket surveillance;
- inspection capability.
For pharmaceutical companies, this suggests that AI governance should increasingly become part of the normal Pharmaceutical Quality System and regulatory-science strategy.
The relevant question is becoming less:
“Do we have an AI policy?”
and more:
“Can we demonstrate that every important AI use remains appropriate, reliable and controlled for its specific regulated purpose?”
Regulatory Status Note
FDA’s Focus Areas of Regulatory Science describe research priorities and ongoing regulatory-science activities.
They are not guidance and do not establish new legally binding CGMP requirements.
FDA also explicitly notes that inclusion of research in the Focus Areas of Regulatory Science does not necessarily imply a determination regarding its future regulatory use.
Nevertheless, the framework provides an important indication of the scientific questions FDA considers relevant to future regulation.
Sources
FDA – Focus Area: Artificial Intelligence:
https://www.fda.gov/ … ificial-intelligence
FDA – Focus Areas of Regulatory Science Report:
https://www.fda.gov/ … atory-science-report
FDA – Lifecycle of FDA-Regulated Products: