The Future of AI in Clinical Research, Delivered Today

Congress has mandated the FDA to modernize regulatory science by adopting more human-relevant, innovative technologies. The goal: to accelerate drug evaluation, improve patient safety, boost R&D productivity, and ultimately lower drug costs. In response, the FDA has issued guidance promoting inclusive patient participation in clinical trials, encouraging the use of digital health technologies for direct data capture, and now outlining how AI can support clinical research.

On April 10, 2025, the FDA announced a major step forward by replacing animal testing with AI models for developing monoclonal antibody therapies and other treatments. The agency shared a roadmap to “begin with monoclonal antibodies for reducing animal use in preclinical safety testing, and then expand to other biological molecules and eventually new chemical entities and medical countermeasures.”

This guidance, titled Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, represents a critical milestone. It paves the way for broader AI adoption across the life sciences, moving from drug discovery and operational improvements to the use of AI-powered data in regulatory submissions assessing safety, efficacy, and quality.

AI and computational modeling will transform life sciences into a golden age of advanced interdisciplinary research and development between biologists, data scientists, and engineers. But with this opportunity comes the need for transparency. To build trust and ensure responsible AI use, organizations must adopt clear, risk-based frameworks.

Central to the FDA’s guidance is a credibility assessment framework which helps establish trust in AI systems by requiring transparency in how training data is collected and used within the defined Context of Use (COU). It includes standards for data quality, training methods, machine learning techniques, and computational algorithms, all of which must be governed by organizational policies and procedures.

AI systems naturally process vast amounts of data – from terabytes to petabytes – across multiple sources and formats, in both batch and real-time. These systems clean, structure, and transform the data into warehouses or data lakes, enabling machine learning (ML) algorithms to identify patterns over time. But having big data alone isn’t enough. If data quality is poor, outcomes can be biased, undermining safety, efficacy, credibility, and financial performance.

Given the scale of data involved, it’s nearly impossible to identify all potential biases up front. Biases may emerge only after AI models have absorbed large datasets and learned complex relationships. Once bias is baked into a model, it doesn’t go away just by removing the original source, just as people don’t forget ideas they’ve learned, even from unreliable sources.

To address this, AI models must be retrained with more representative data and use techniques like counterfactual data augmentation, adversarial training, or fairness constraints. All of this requires a structured risk assessment process, supported by modern data capture platforms and internal policies.

Crucial Data Solutions’ (CDS) eClinical platform, TrialKit, is designed to align with FDA guidance and help life sciences organizations harness AI responsibly. TrialKit offers a fully configurable Software-as-a-Service (SaaS) platform for capturing clinical study data. That data can feed into proprietary, organization-wide data warehouses without leaving the unified platform.

TrialKit’s Platform-as-a-Service (PaaS) offering includes more than 1,500 Application Programming Interfaces (APIs) to enable real-time or batch data integration from any cloud system. These APIs can capture information from third-party systems and apply logic to anonymize, transform, and validate data before it enters your warehouse or data lake, ensuring quality and compliance before AI processing begins. Unlike standard integration platforms (iPaaS), TrialKit’s enhanced PaaS includes a visual drag-and-drop tool that lets subject matter experts build customized workflows. These workflows align with internal procedures and are easy to manage across organizational hierarchies.

Additionally, TrialKit includes an award-winning Data-as-a-Service (DaaS) model that supports AI-driven drug discovery and can be configured for machine learning, helping organizations conduct clinical studies on a regulatory-compliant platform.

With this combination of SaaS, PaaS, and DaaS capabilities, CDS empowers life sciences companies to centralize their clinical and third-party data in one compliant environment ready for AI ingestion and regulatory reporting. Now, imagine having access to all your clinical and preclinical study data captured from competing platforms, remote patients using wearables, ingestibles, or proximity sensors, ePRO/eCOA, CoreLab blood assays, DICOM imaging, adjudicated assessments, and more. All that data, clean and secure, ready for the future of AI-driven clinical research.