This talk highlights practical ways agentic AI can reduce routine manual work across clinical development. We’ll look at real, low-risk applications—automating feasibility summaries, preparing study-startup packages, checking protocol criteria, cleaning site data, and tracking actions from monitoring visits. Emphasis is placed on what is dependable today, how to implement it safely, and where human oversight remains essential.

Recent breakthroughs have enabled LLMs to carry out multi-step scientific tasks through sophisticated tool use, reasoning, and orchestration. We develop an agentic AI framework to assist with decision making on clinical programs. This framework consists of multiple agents which integrate evidence from experimental data and scientific text to address complexity of the modern clinical programs. This framework demonstrated promising results while applied to several clinical programs.

Getting the right therapy to the right patient at the right time requires a patient-centric evidence approach. We share advanced modeling methods that enable enriched patient understanding using structured and unstructured medical information. We apply these techniques on topics like patient journey to identify predictors of loss of control. We combine these insights with patient panels, literature, physicians, and health technology assessment input to shape disease strategy and accelerate protocol and development planning decisions. Lastly, we show the promise of these approaches beyond development into medical to support launched products.

We have designed and implemented a clinical-trial forecasting system powered by ML models and heterogeneous data using software, modeling, and systems best practices. Our flexible and scalable architecture allows us to rapidly iterate our models while maintaining a clear line of sight to source data and results. If Biogen can build this ourselves, other pharmaceutical companies can too.

We are transforming the way we are running clinical trials leveraging a new concept: agentic AI. Our new AI-powered assistant will transform analytics from passive dashboards into proactive insights and actions, supporting clinical teams to be aware of key activities they need to take, organize their day and even take some actions for them.

We’ll share use cases of how we implemented an operating model with data-driven decision-making at its core. Leveraging the availability of more data than ever, we changed our ways of working to make the most out of it.

Purpose: To automate and expedite the creation of the Lay Protocol Synopsis (LPS) and Brief Summary (BS), which are time-sensitive regulatory documents needed for effective communication with ethics committees, patients, and the public. Method: Utilizes a library of prompts mapped to the CTP, along with a blend of rule-based template language, large-language models (LLMs), and retrieval augmented generation (RAG) to produce an initial draft of the LPS.

This panel explores how artificial intelligence is transforming patient engagement across the clinical trial lifecycle. Industry experts will discuss AI-driven strategies for identifying eligible participants, accelerating recruitment, enhancing retention through personalized communication, and predicting potential dropouts. Learn how data analytics and machine learning are optimizing recruitment efficiency, improving patient engagement, and facilitating successful study outcomes.

A robust data framework is essential for effective AI implementation in clinical research. It ensures high-quality, interoperable, and secure data across diverse sources, enabling reliable model development and regulatory-grade insights. By integrating standardized data structures, governance, and validation processes, such a framework accelerates evidence generation, supports ethical AI use, and enhances the efficiency and impact of clinical research.