Artificial Intelligence/Machine Learning

Raja Shankar, VP of machine learning at IQVIA, explains how AI-driven trial simulation and automation are beginning to influence decision-making across every phase of clinical development.

When a single pivotal trial can determine the fate of an entire program, success depends less on marginal gains in speed or cost and more on building robust, adaptive trial designs that actively manage uncertainty and protect the probability of a positive outcome.

The FDA and EMA have aligned on ten guiding principles for the responsible use of artificial intelligence across the drug development lifecycle, establishing a shared framework to support innovation, regulatory consistency, and patient safety.

A look at how efficiency, access, platformization, AI, non-traditional players, and regulatory recovery are expected to reshape clinical operations in 2026.

Examine how practical AI applications can streamline contracts and startup workflows while preserving the central role of investigators, site staff, and patient relationships in clinical research.

Explore how contract standardization, proactive budget alignment, and AI-enabled negotiation tools can reduce site activation delays and turn agreements into strategic accelerators rather than administrative bottlenecks.

As AI-driven search becomes the primary way patients discover research opportunities, the quality and structure of clinical trial registry data will determine whether transparency translates into real, equitable access.

See what will distinguish sponsors that scale AI into core operations from those stuck in experimentation, and why redesigning underlying processes—not just optimizing workflows—is critical to realizing long-term value.

The clinical trial ecosystem is entering a phase of consolidation and reinvention driven by the collapse of boundaries between functions, data, and even companies themselves.

Unpack how rising competition for the same high-profile sites is slowing startup and enrollment—and what sponsors must change in their site strategies to ensure AI-enabled efficiencies translate into real-world impact.

BostonGene has entered a new collaboration with AstraZeneca to apply its foundation AI model to oncology drug development, aiming to improve early trial decision-making around safety, efficacy, and biomarker strategy while accelerating clinical timelines.

Learn why combining AI-enabled trial matching with transportation, lodging, and financial assistance is essential to turning trial eligibility into actual participation—and why matching alone is not enough.

Learn how AI-enabled automation can streamline existing operational processes, reduce manual effort, and enhance efficiency while allowing sites to work as they do today.

New research finds that while eSource adoption is advancing through EHR-to-EDC workflows, scaling its impact will depend on integrating unstructured clinical data using AI, shared standards, and collaborative validation models across sites, sponsors, and vendors.

Learn how clinical operations leaders can build technology adoption, transform workflows, and cultivate digitally savvy talent to successfully implement AI across the entire clinical trial lifecycle.

New data show that applying AI to the migration of translated COAs into eCOA platforms can meaningfully reduce errors, accelerate localization workflows, and support broader global patient participation—while still relying on human reviewers to ensure linguistic precision and clinical integrity.

See how combining human oversight with AI insights improves protocol authorship, site selection, and monitoring strategies, delivering better decisions than AI-only or human-only approaches.

Explore how AI can optimize study design, speed patient recruitment, and streamline operational workflows to shorten development timelines and enhance trial efficiency.

AI-driven discovery, EHR-based real-world evidence, and synthetic patient modeling are rapidly reshaping drug repurposing, reducing development timelines, expanding therapeutic applications, and accelerating regulatory acceptance of computational approaches.

The FDA has introduced a secure, agency-wide agentic AI system to support complex regulatory workflows—enhancing reviews, surveillance, and inspections—while maintaining strict data protections and human oversight.

As clinical trials become more complex and decentralized, the role of the CRA is evolving from site monitor to strategic, digitally fluent partner, requiring expertise in hybrid oversight, risk-based monitoring, and multi-system data management.

As AI adoption accelerates across clinical research, clear distinctions between AI agents, AI teammates, and integrated intelligence are essential for understanding how automation will reshape site operations, workforce roles, and end-to-end study processes.

AI-enabled automation is rapidly moving into routine pharmacovigilance operations, streamlining case intake and processing, reducing longstanding adoption barriers, and driving new efficiencies as sponsors and functional service provider partners scale safety workflows.