Artificial intelligence is rapidly becoming part of clinical development. But adoption alone does not create value. For sponsors, the more important question is where AI can meaningfully improve a decision, reduce friction, or strengthen evidence generation.
Clinical development is complex, resource-intensive, and increasingly data-rich. Teams make decisions across protocol design, patient identification, trial execution, safety monitoring, data review, and analysis. AI can support many of these activities, but not every use case delivers meaningful clinical or operational value.
The strongest opportunities tend to be those where AI helps teams make better-informed decisions, work more efficiently, or identify useful insights sooner—while maintaining appropriate human oversight.
1. Improving Trial Design
AI can help clinical development teams analyze large and complex datasets to inform trial design. Potential applications include evaluating patient characteristics, exploring eligibility criteria, assessing endpoints, characterizing pharmacokinetic profiles, and modelling different study scenarios.
For example, AI can help evaluate whether eligibility criteria may unnecessarily constrain the recruitable patient population without meaningfully advancing the study objectives. It can also help teams explore patient-selection strategies or dosing scenarios before committing to a final design.
The value is not simply faster analysis. Better-informed design choices early in development can help reduce avoidable complexity and improve the usefulness of the evidence a study ultimately generates.
2. Finding and Engaging the Right Patients
Patient identification, enrollment, and retention remain persistent challenges in clinical trials. AI can help analyze clinical and real-world data to identify potentially eligible patient populations, support patient-to-trial matching, and inform site-selection or recruitment strategies.
AI-enabled approaches may also support participant engagement and retention. FDA has discussed potential applications such as medication-adherence monitoring, electronic medication tracking, missed-visit detection, participant-support tools, and the use of data to identify participants who may be at greater risk of dropping out.
The practical value is not automation for its own sake. It is the potential to reduce manual effort, surface relevant information earlier, and help teams connect appropriate patients with appropriate studies while preserving human oversight.
3. Turning Clinical Data Into Earlier Insights
Clinical development generates large volumes of structured and unstructured data across multiple systems and sources. AI and machine learning can help identify patterns, detect anomalies, prioritize information for review, and support analysis of complex datasets.
These capabilities may help teams surface emerging safety or operational signals earlier and focus expert attention where it is most needed. But speed alone is not enough. The usefulness of an AI-generated output depends on the quality of the underlying data, the model’s intended purpose, and whether its performance is sufficiently reliable for that use.
When AI-generated information is intended to support regulatory decision-making, model credibility becomes particularly important. FDA’s January 2025 draft guidance proposes a risk-based credibility assessment framework centered on a clearly defined context of use—the specific role and scope of the model in addressing a question of interest.
4. Supporting Better Development Decisions
As clinical evidence accumulates, AI can help teams synthesize information, evaluate emerging patterns, and explore potential scenarios. This can give clinical, statistical, regulatory, and operational experts better information as they consider development decisions.
The distinction is important: AI should support decision-making, not replace scientific judgment or accountability. The value of the technology depends on how well it fits into the broader decision process, including appropriate review, controls, and human expertise.
Start With the Decision, Not the Technology
The most useful question is rarely, “Where can we use AI?” A better starting point is: “What decision are we trying to improve?”
From there, sponsors can ask:
- What data are available and are they fit for the intended purpose?
- How could AI improve the quality, speed, or consistency of the decision?
- What risks does the application introduce?
- What level of human oversight is appropriate?
- How will performance be evaluated and monitored over time?
This decision-first approach is consistent with the direction regulators are taking. In January 2026, FDA and the European Medicines Agency published joint Guiding Principles of Good AI Practice in Drug Development. The principles emphasize human-centric design, risk-based approaches, a clear context of use, multidisciplinary expertise, data governance and documentation, performance assessment, and lifecycle management, among other considerations.
For sponsors, AI should not sit alongside the development strategy as a separate technology initiative. It should be considered in the context of the development decisions the program needs to make.
The goal is not to use AI everywhere. It is to use AI where better information, faster analysis, or more efficient execution can meaningfully improve clinical development decisions.
References
[1] U.S. Food and Drug Administration. The Role of Artificial Intelligence in Clinical Trial Design and Research with Dr. ElZarrad.
[2] U.S. Food and Drug Administration. Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products. Draft Guidance for Industry and Other Interested Parties. January 2025.
[3] U.S. Food and Drug Administration and European Medicines Agency. Guiding Principles of Good AI Practice in Drug Development. January 2026.
