4 foundations for making AI and data ready for clinical development

AI is becoming a core component of drug development and clinical research. Models are improving and regulatory frameworks are evolving, but progress still depends on the quality and structure of the underlying data. To move from experimental AI to operational AI, organizations need datasets that are traceable, standardized, and auditable from source to model output.

Across the industry, the gap between experimental AI and operational AI remains wide. Many teams can generate promising models, but few can deploy them confidently in regulated settings. In many cases, this is due to challenges in data quality and infrastructure.

This blog explores what it takes to make datasets AI-ready and trial-ready, and how that foundation enables AI to deliver reliable insights that can be applied in real-world research and clinical settings.

1. Traceability

A dataset is considered “traceable” when every element can be linked to its source and understood in context. That includes who generated the data, what instruments or assays were used, and what preprocessing steps were applied. When these details are missing or undocumented, reproducibility becomes almost impossible. In genomic research, even subtle inconsistencies can distort findings significantly.

The growing regulatory focus on data provenance reflects this concern. Agencies expect that when AI influences study design, endpoint selection, or risk prediction, sponsors can document the full chain of data custody and processing. While this level of documentation may appear to slow innovation, it is intended to ensure that AI outputs can be interpreted and validated with the same rigor as any other evidence used in clinical research. 

Sponsors building datasets for AI and trials that will face regulatory or payer scrutiny must invest in data infrastructure and documentation up front to avoid facing obstacles later in development. That means asking practical questions early to determine whether an algorithm can be validated and reused, such as:

• Are data sources consistent in how they define and measure key variables?
• Can each data point be traced to its origin without ambiguity?
• Is there a process to identify and resolve inconsistencies before the data reach model training?

2. Interpretability

AI interpretability remains one of the most persistent challenges in genomics, where models often analyze millions of data points. Deep learning models, in particular, can achieve high accuracy but often provide limited insight into why a specific output was generated. For clinicians and regulators, this lack of transparency limits trust and usability.

Recent work in functional genomics shows that interpretability is not a single technique but a design principle that spans data, model architecture, and analysis. Effective interpretability begins with choosing the right model class for the biological question. 

Once a model is trained, developers can use two main strategies. Active approaches integrate biological knowledge directly into the model, such as by embedding gene ontology or pathway information so that outputs can be traced to known biological entities. Passive approaches work post-hoc, applying interpretation algorithms to estimate which inputs drive predictions. 

Interpretability also operates at multiple levels. Local methods help explain individual predictions, for example identifying which genes most strongly influence a patient-specific risk score. Global interpretations summarize patterns across entire populations, revealing which features consistently drive predictions across datasets. Combining both is now considered best practice because it provides individual understanding and generalizable insight.

Models that can explain their reasoning can be audited and validated. This allows teams to compare outputs, retrain models responsibly, and identify when predictions fall outside their intended scope.

3. Bias mitigation

Bias is both an ethical concern and a technical limitation. If the data used to train a model reflect only certain populations or clinical settings, the model will perform inconsistently when applied in new contexts. This reduces reliability for global trials or diverse patient populations.

Mitigating bias starts with understanding the data landscape. Sponsors can assess how representative their datasets are, document demographic and clinical coverage, and test performance across subgroups. These steps help identify where new data collection could expand access or improve trial inclusivity.

Emerging methods such as bias-aware modelling make it possible to train models on distributed datasets without moving sensitive data, improving both privacy and diversity. The ability to train across multiple data sources while maintaining governance is becoming increasingly important as regulators expect stronger evidence of fairness and generalizability in AI systems.

4. Regulatory alignment and data readiness

Regulatory agencies are increasingly focused on the data practices that support AI systems in clinical development. Guidance from the FDA and EMA highlights transparency, documentation, and reproducibility as key requirements. Sponsors need to show how data is sourced and processed, how models are trained and validated, and how potential risks are managed.

Agencies also promote early engagement when AI is applied in study design, endpoint assessment, or population enrichment. Dedicated forums allow sponsors to discuss planned approaches before submission and to align expectations around evidence requirements.

Adopting Good Machine Learning Practice principles and maintaining audit-ready documentation across the AI lifecycle supports both compliance and reproducibility. Embedding these standards within existing data quality and GCP systems creates a durable foundation for trustworthy and review-ready AI.

Conclusion

Developing datasets that are both AI-ready and trial-ready requires consistent attention to data integrity, structure, and governance. Organizations that embed data readiness into their scientific and operational workflows will move with greater confidence and efficiency. The same practices that meet regulatory expectations also improve collaboration, accelerate discovery, and ensure that AI contributes to reliable, transparent progress in clinical research.

To explore how AI is transforming early discovery and development, read our latest whitepaper AI-Driven Drug Discovery in 2025: Platforms, Pitfalls, and Progress, which examines current platforms and emerging standards for integrating AI into R&D pipelines.