The first half of 2026 brought a steady stream of regulatory developments related to real-world data (RWD) and real-world evidence (RWE). While the individual updates span over 5 regions and regulatory contexts, a common theme emerges: RWE is increasingly embedded into how evidence is generated, evaluated, and maintained across the product lifecycle. Rather than serving solely as a supplementary source of information, RWE continues to be incorporated into broader evidence-generation programs that support regulatory and healthcare decision-making.

Table 1 summarizes major real-world regulatory policy developments from January through June 2026. Taken together, these updates suggest that regulators globally are increasingly focused on how RWE can be incorporated into comprehensive evidence-generation strategies.

RWE as an Integral Part of the Evidence-Generation Framework

Several of the most consequential developments this year reflect a continued shift toward integrating RWE directly into development and regulatory decision-making.

The adoption of International Council for Harmonisation (ICH) M14 by the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), and National Medical Products Administration of China (NMPA) establishes internationally aligned expectations for non-interventional safety studies using RWD. At the same time, ICH E6(R3) Annex 2 expands Good Clinical Practice (GCP) principles to innovative and decentralized trial designs that may leverage RWD. Together, these initiatives provide greater clarity and consistency around how evidence for regulatory decision-making can be generated from global RWD sources.

Regulatory agencies are also advancing new methodological approaches for integrating RWE into clinical development programs and regulatory decision-making. In the U.S., recent updates include FDA guidance on Bayesian methodologies, the Plausible Mechanism Framework for individualized therapies, and revisions to draft guidance on demonstrating substantial evidence of effectiveness. In Europe, EMA's final RWD chapter of the European Union (EU) Data Quality Framework, adopted by the Committee for Medicinal Products for Human Use (CHMP) in March 2026, further clarifies expectations for data quality, governance, traceability, and transparency for the use of RWD in regulatory decision-making. Similar efforts are emerging globally, including the Saudi FDA's final framework on the use of RWD and RWE, which provides additional guidance on the evaluation and use of RWE.

Alongside these policy and methodological developments, regulators continue to invest in the infrastructure and engagement mechanisms needed to operationalize RWE for regulatory use. EMA's continued expansion of the Data Analysis and Real-World Interrogation Network (DARWIN EU) support evidence generation across Europe, while programs such as the Medicines and Healthcare products Regulatory Agency (MHRA) RWE Scientific Dialogue program provide sponsors with opportunities for early regulatory engagement.

Across these examples, regulators are increasingly acknowledging the role of external controls, natural history studies, and other innovative evidence-generation approaches, particularly in settings where traditional trial paradigms may be challenging or infeasible to execute. This evolution is reflected in initiatives such as the Friends of Cancer Research (FoCR) work on the application of external control arms in oncology drug development, which has brought together regulators, industry, and other stakeholders to evaluate the use of real-world data and federated analytic approaches to support more robust and scalable external control methodologies. IQVIA has contributed to these efforts through participation in both the public discussions and pilot activities supporting the initiative. IQVIA is also a founding member of the RWE Alliance, a coalition of real-world data and analytics organizations that engages with regulators and policymakers to advance the appropriate use of RWD and RWE in regulatory decision-making. Through the Alliance, IQVIA contributes to policy discussions, regulatory engagement activities, and public comment submissions aimed at furthering the development of RWE frameworks and standards.

Similarly, IQVIA is engaged in collaborative efforts such as the GetReal Institute's External Comparator Framework workstream and the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) initiative to develop a supplement to the Good Practice Guide for the use of the HMA-EMA Catalogues of RWD Sources and Studies demonstrate how multi-stakeholder groups are translating emerging concepts into practical methodological guidance. Such efforts highlight the growing regulatory interest in developing frameworks that enable innovative evidence-generation approaches while maintaining scientific rigor.

From Policy Development to Implementation and Learning

Another notable trend is the increasing emphasis on implementation.

Early discussions around use of RWE drug development focused on establishing principles and potential use cases. Today's conversations are increasingly informed by practical experience. Regulators now have a growing portfolio of demonstration projects, pilot programs, and real-world examples that illustrate how RWE can be applied in practice.

This emphasis on transparency and practical learning is evident in efforts to document and share regulatory experience with RWE. Examples include EMA's fourth annual report on its RWE framework to support EU regulatory decision-making, updates from the FDA-funded Randomized Controlled Trial to Duplicate Clinical Trials Using Real-World Data (RCT-DUPLICATE) initiative, expansion of MHRA and National Institute for Health and Care Excellence (NICE) RWE Scientific Dialogue programs, and FDA's catalog of regulatory decisions supported by RWE across both drugs and medical devices. Notably, the 2026 Center for Devices and Radiological Health (CDRH) report highlighted 73 examples of how RWE informed regulatory decision-making between 2020 and 2025, including an IQVIA and Roche Diagnostics collaboration that used linked RWD from the IQVIA and National Football League (NFL) COVID-19 occupational testing program to support expansion of a SARS-CoV-2 diagnostic test labeling for symptomatic to asymptomatic populations. This example illustrates how fit-for-purpose RWD can complement traditional evidence generation approaches to support regulatory decisions and broaden patient access to important diagnostic technologies.

The medical device ecosystem has shown particularly notable momentum, with updates from Health Canada, the National Evaluation System for health Technology Coordinating Center (NESTcc), the Medical Device Coordination Group (MDCG), and FDA reinforcing the expanding role of RWE across pre-market and post-market settings.

As these examples accumulate, sponsors have an increasingly robust body of precedent to inform study design, evidence strategy, and regulatory engagement. Collectively, these updates highlight continued emphasis on the data access, methodological, operational, and governance considerations required to ensure that RWE is fit to support regulatory and healthcare decisions.

Artificial Intelligence Appears to Be Following a Similar Regulatory Journey

Many of the regulatory activities emerging around artificial intelligence (AI) closely resemble the journey RWE has followed over the past decade. Regulators globally are publishing guiding principles, launching pilot programs, creating governance frameworks, investing in infrastructure, and seeking ongoing dialogue with industry.

Recent examples include the FDA-EMA guiding principles for AI in drug development, the EMA–Heads of Medicines Agencies (HMA) 2026-2028 Data and AI Workplan, FDA's AI-enabled clinical trial pilot activities, NMPA’s ‘Implementing Opinions’ on advancing AI-enabled drug regulation and lifecycle oversight, and the continued expansion of regulatory AI capabilities and infrastructure.

This trajectory mirrors the evolution of RWE policy, where regulatory frameworks matured through years of stakeholder engagement, scientific discussion, demonstration projects, and practical application. Rather than emerging fully formed, regulatory expectations evolved alongside advances in methodology, technology, and evidence generation.

The implication is that continued industry engagement remains critical. As regulators and sponsors gain experience with AI-enabled approaches, today's frameworks will continue to evolve to reflect emerging use cases, operational realities, and lessons learned.

The Future of AI and RWE May Share the Same Foundation

The regulatory and methodological questions emerging around AI are strikingly similar to those the industry has spent the last decade addressing in RWE generation.

Across both domains, regulators continue to emphasize data provenance, traceability, quality, governance, transparency, documentation, validation, and lifecycle oversight. These principles are central to recent guidelines and standards such as ICH M14, EMA's Data Quality Framework, and ICH E6(R3), but they are equally relevant to emerging conversations around AI governance and trustworthy AI in healthcare. As organizations move from AI principles to implementation, increasing attention is being paid to how performance, reliability, and fitness for purpose should be demonstrated in practice. Collaborative efforts such as the DIA AI Consortium, which brings together participants from regulatory agencies, academia, and industry, including IQVIA, are helping shape practical frameworks for risk-proportionate validation, governance, monitoring, and documentation of AI systems across the healthcare ecosystem.

AI is amplifying many of the same questions regulators and industry have already been working through in the context of RWE. In many respects, the foundational investments organizations have made to support the use of RWE for regulatory purposes, including data quality frameworks, interoperable data models, governance processes, and evidence transparency, are likely to serve as important enablers of responsible AI adoption.

As regulators continue to define principles and methodologies for validating AI systems, industry engagement will play an important role in shaping practical approaches that are both scientifically rigorous and fit for purpose. We anticipate that regulators may increasingly look to advanced applications, such as AI-enabled literature review tools, as real-world examples of how validation can be operationalized in practice. One example is the IQVIA Literature AI platform, which helps researchers search, screen, extract, and synthesize evidence from large volumes of scientific literature. Its development reflects a risk-proportionate approach to validation, where validation activities are aligned with the tool's intended use, potential impact, and level of human oversight. Such examples can help inform ongoing regulatory discussions around how to demonstrate that AI systems are reliable, transparent, and appropriately governed for their context of use.

Looking Ahead

The regulatory developments highlighted in Table 1 reflect a broader shift in how evidence generation is evolving globally. RWE is increasingly being integrated across clinical development, regulatory review, and post-market monitoring, while AI for drug development appears to be following a familiar path shaped by iterative learning, stakeholder engagement, and evolving regulatory science.

For organizations developing medicines, diagnostics, and medical technologies, these developments provide insight into how regulatory expectations are evolving across RWE, data quality, and AI. Understanding these trends can help inform future evidence-generation strategies, data investments, and regulatory planning in an environment where both RWE and AI continue to play an increasingly important role.

Table 1. Key RWE & AI Regulatory Policy Developments January – June 2026 (non-exhaustive)