AI + Real-World Data: Accelerating What’s Possible in Clinical Research

Healthcare generates more data today than at any point in history. Yet despite billions of data points created daily, much of it remains fragmented, underutilized, and locked away in formats too complex to analyze. Over 70% of healthcare data is unstructured, embedded in physician notes, lab results, and diagnostic reports. But most real-world data (RWD) initiatives still rely heavily on structured fields such as claims codes and billing data.

This gap matters because real-world data, when properly analyzed, has the power to become real-world evidence (RWE)—insights that not only support clinical trials but also guide regulatory decisions, inform practice guidelines, and improve patient outcomes. The challenge is moving from messy, siloed data to reliable, actionable evidence. Artificial intelligence (AI) is poised to bridge that gap.

What Exactly Is RWD and RWE?

Real-world data refers to healthcare information routinely collected outside the context of randomized controlled trials: electronic health records (EHRs), insurance claims, registries, even patient-reported outcomes. Real-world evidence is what emerges when that data is analyzed to answer specific questions about safety, effectiveness, or population-level impact.

Regulators have already begun to embrace this shift. The U.S. Food and Drug Administration (FDA) has issued guidance on using RWE to support drug approvals and label expansions. Payers are demanding evidence that therapies work in the populations they serve. And clinical researchers are increasingly turning to RWD to complement trial findings with insights from everyday practice.

Why AI Is the Missing Link

The promise of RWD is immense — but the execution has been limited by the complexity of the data itself. Traditional approaches, such as manual chart abstraction or reliance on ICD codes, are slow, expensive, and often incomplete.

AI changes the equation. With natural language processing (NLP) and large language models (LLMs), researchers can now parse unstructured clinical text at scale. AI makes it possible to:

• Extract nuanced details from free-text physician notes, radiology reports, and lab results.

• Standardize disparate sources into structured insights suitable for analysis.

• Improve precision in identifying trial-eligible patients or measuring real-world treatment outcomes.

• Reduce bias by capturing the full patient story rather than just surface-level codes.

By transforming raw healthcare data into standardized, research-ready insights, AI is enabling RWE that is both faster and more representative.

Real-World Applications in Clinical Research

The integration of AI with RWD is already beginning to reshape clinical research in several critical ways:

Patient Recruitment
RWD enhanced by AI can identify patients who meet complex trial criteria, accelerating enrollment and reducing screen failure rates.

Trial Feasibility
AI-powered feasibility assessments help sponsors forecast patient availability across geographies, age groups, and disease subtypes.

Regulatory Submissions
Post-market safety studies and label expansions increasingly rely on RWE. AI ensures these studies are powered by accurate, context-rich data.

Health Equity
By surfacing demographic and social determinants of health from unstructured sources, AI helps ensure underrepresented populations are not overlooked in research.

Challenges That Still Need Solving

The potential is clear, but there are challenges to address:

• Data quality and harmonization: Integrating EHRs, claims, and registries requires alignment on standards.

• Regulatory validation: Agencies require transparency into how AI-derived evidence is generated.

• Ethical use of AI: Patient privacy must remain paramount, with safeguards against misuse.

• Human oversight: Clinical-grade accuracy demands that AI outputs are reviewed and validated by medical experts.

These considerations highlight why collaboration across technology, healthcare, and research communities is essential.

RWD + AI: Driving the Next Phase of Clinical Research

The next phase of clinical research will be defined by the convergence of AI and real-world data. Community health practices will increasingly become data hubs, contributing insights that extend far beyond traditional trial boundaries. Sponsors and regulators will depend on AI-driven approaches to ensure that evidence reflects real-world patients, not just trial populations.

The industry is at a turning point: AI is no longer a futuristic concept, but a practical tool transforming how data is collected, interpreted, and applied. By unlocking the full value of RWD, AI is accelerating discovery, reducing bottlenecks, and making research more inclusive.

For example, a Brookings Institution article explores how postmarket evidence development and real-world data can accelerate innovation and improve regulation of medical products.

Why This Shift Matters Now

AI is enhancing clinical trials. By connecting trial rigor with the realities of everyday care, AI-powered RWD is helping the industry move toward faster, smarter, and more patient-centered research. The result is science that not only advances medicine but also better reflects the world we live in.