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Pharmaceutical companies can make AI useful by starting with a specific evidence task, defining how its output will inform a decision, and assessing the model for that exact use. Data fitness, the consequences of error, human responsibility, and ongoing monitoring all shape whether an AI output is fit to support work across the drug development life cycle. AI can assist that work; it does not replace scientific judgment or regulatory standards.
How can pharma turn evidence into action with AI?
Begin with the decision, not the technology. A team should be able to state what evidence it needs, what the AI system is expected to produce, who will use that output, and what action it could inform. That description defines the model’s context of use. It also gives the team a basis for deciding what data, performance evidence, review, and controls are appropriate.
This is consistent with the FDA and EMA’s January 2026 Guiding Principles of Good AI Practice in Drug Development. The principles frame responsible AI practice across drug development; they are intended to inform good practice and future collaboration, not to replace the legal and regulatory frameworks that apply in particular jurisdictions. Their use of “drug” encompasses U.S. drugs and biological products and EU medicinal products.
The FDA’s January 6, 2025 announcement quoted Commissioner Robert M. Califf, M.D., saying: “With the appropriate safeguards in place, artificial intelligence has transformative potential to advance clinical research and accelerate medical product development to improve patient care.” The safeguards condition matters: an AI-generated result is useful only when its role and reliability are understood well enough for the decision at hand.
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Where can AI help across the drug development life cycle?
The FDA’s January 2025 draft guidance describes examples of AI supporting information or data for regulatory decision-making. These are possible applications, not blanket endorsements or evidence that any particular implementation meets regulatory expectations.
| Area | Example described by FDA | Question to define before relying on the output |
|---|---|---|
| Nonclinical development | Reducing animal-based pharmacokinetic, pharmacodynamic, and toxicologic studies | What evidence would show that the model is suitable for the specific decision or study it is intended to support? |
| Clinical pharmacology | Predictive modeling for clinical pharmacokinetics and exposure-response analyses | Does the model perform adequately for the relevant population, data, and intended analysis? |
| Disease understanding and trials | Integrating natural-history, clinical-study, genetic-database, clinical-trial, social-media, and registry data to better understand disease presentation and progression | Are the data relevant, sufficiently documented, and appropriate to combine for this purpose? |
| Endpoints and outcomes | Analyzing real-world and digital-health data for trial endpoints or outcomes | Does the output support the endpoint or outcome as defined for the trial, and are the data fit for that use? |
| Postmarketing | Processing adverse drug experience information | How will people review, interpret, and act on the processed information? |
| Manufacturing | Selecting manufacturing conditions | What is the consequence of an incorrect recommendation, and what verification and oversight are needed? |
These examples span different decisions and consequences. A model used to organize incoming information is not automatically comparable to one whose output could influence a safety assessment or manufacturing condition. The intended use, not the fact that both systems use AI, determines what credibility evidence and oversight are appropriate.
How should a pharmaceutical company adopt AI in drug development?
A practical adoption process can translate the regulator principles into five connected steps. This is an implementation approach based on those principles and the FDA draft framework, not a regulator-prescribed checklist or a universal process.
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- Map the consequences of error. Consider what could happen if an output is wrong, incomplete, delayed, or unrepresentative. Set validation, mitigation, and human-review controls in proportion to that risk and the model’s role.
- Assess the data and document the work. Examine whether data are relevant and governed appropriately for the stated task; record their provenance, processing, limitations, and role in model development or evaluation. Involve the scientific, clinical, statistical, regulatory, data, technical, and other expertise the use requires. No data source is fit for a purpose merely because it is available.
- Evaluate performance for the intended use. Ask whether the supporting evidence demonstrates performance in the defined context. A favorable generic benchmark alone does not establish credibility for a distinct population, decision, or operating setting.
- Plan for operation and change. Establish who monitors the system, who can act on its output, how issues are escalated, and how documentation and governance will be maintained as the model or its use evolves. Make essential information and human responsibilities clear to the people who rely on the system.
How do you assess whether an AI model is credible for regulatory use?
“Credible” is not a universal label attached to a model. It refers to whether the evidence supports a model for a particular context of use. FDA’s January 2025 Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products proposes a risk-based credibility assessment framework for models that produce information or data intended to support regulatory decisions about safety, effectiveness, or quality.
That FDA document was issued as a draft Level 1 guidance, marked “Not for implementation,” and described as containing nonbinding recommendations. It is a proposal, not a finalized rule or binding requirement. Guidance status can change; consult FDA’s current guidance listing before relying on the document as current. The draft is specific to U.S. drug and biological product decisions. The joint FDA–EMA good-practice principles provide a broader cross-agency frame, but neither should be read as eliminating jurisdiction-specific obligations.
For an actual use case, assess the evidence against questions such as these:
- Task: Is the intended output and its role in the decision clearly stated?
- Risk: What are the consequences if the output is wrong, missing, or not representative, and are controls proportionate?
- Data: Are relevance, governance, provenance, limitations, and documentation understood for this use?
- Performance: Does evaluation address the intended context rather than relying only on a general benchmark?
- Standards and expertise: Have relevant technical, scientific, cybersecurity, ethical, and GxP standards and multidisciplinary expertise been considered?
- Oversight and lifecycle: Are human responsibilities, monitoring, change management, and essential information clear?
The January 2026 principles organize good practice around ten areas: human-centric design; a risk-based approach; adherence to relevant standards; a clear context of use; multidisciplinary expertise; data governance and documentation; model design and development practices; risk-based performance assessment; life-cycle management; and clear, essential information. Together, these areas help teams ask whether an AI system is responsibly designed and managed, while the FDA draft supplies a proposed way to think about credibility for a specific regulatory context.
What do the FDA’s adoption figures show—and not show?
FDA’s AI in Drug Development overview reports that CDER had experience with more than 500 drug and biological product submissions with AI components from 2016 to 2023. The same overview reports that over 800 external comments on the May 2023 discussion paper informed the 2025 draft guidance.
These counts describe submissions and comments, respectively. They do not measure how well the AI systems performed, whether submissions were accepted, how much time or money AI saved, or the return on investment of a general adoption program. They indicate regulatory experience and engagement, not proof that any particular model or approach is effective.
What should leaders measure after adoption?
Set success criteria around the evidence task and decision defined at the outset. A team might track whether the system produces the required output reliably in its intended setting, whether reviewers can identify and resolve problematic cases, and whether governance and documentation remain current. The specific measures should follow the use case and its risks; a single accuracy figure or productivity target cannot establish credibility across all pharmaceutical applications.
Keep decision authority explicit. Teams should know which outputs are advisory, which require verification, who is accountable for consequential decisions, and what happens when performance or data conditions change. AI adoption is therefore an operating model as well as a technical project: it connects evidence needs, model assessment, expertise, controls, and life-cycle management.
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