Artificial Intelligence/Machine Learning

Angela Zubel, chief development officer, Debiopharm, emphasizes that organizations willing to standardize data and adopt practical AI tools are already gaining efficiency, cost savings, and stronger real-time oversight across development programs.

Angela Zubel, chief development officer, Debiopharm, explains how AI-enabled site selection, patient allocation, and real-time data monitoring can reduce costs, shorten timelines, and limit inefficiencies caused by non-performing sites.

Angela Zubel, chief development officer, Debiopharm, discusses why 2026 marks a shift from AI pilots to broader operational implementation across clinical trials and drug development programs.

Mike Wenger, chief innovation officer at CRIO, explains how AI can responsibly support data quality and monitoring with proper oversight, and why advancing eSource and EHR systems remains critical to strengthening data integrity and remote trial operations.

Under a new strategic collaboration, Bristol Myers Squibb will deploy Evinova’s AI-native Study Designer platform to optimize trial design, improve decision-making, and drive efficiencies across its global clinical portfolio.

Raja Shankar, VP of machine learning at IQVIA, discusses which AI capabilities sponsors are most likely to adopt first to streamline trial workflows and reduce operational burden, while also highlighting emerging applications that could shape the next phase of clinical trial design.

As clinical trials grow more global and complex, AI is emerging as a practical enabler of smarter financial management by automating manual processes, improving visibility across fragmented systems, and helping sponsors, CROs, and sites reduce delays, errors, and operational friction.

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.

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.

Examine the strategies community research sites can use to secure trial opportunities, from adopting AI-enabled workflows to proving verified access to underrepresented patient populations.

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.