The presentations in this section will focus upon the digital transformation of clinical development through AI-driven innovation and data-centric approaches. Topics include GenAI applications for document creation and protocol design, organizational adoption of AI/ML, and the importance of FAIR data and semantic harmonization. Together, they emphasize how integrating advanced technologies with streamlined processes empowers faster insights, reduces burdens, and drives more effective, patient-centered research.

The digital transformation of Clinical Development goes beyond simple digital optimization and combines people, processes, and technology with the following core tasks: Treating data as an asset, fostering a data-driven mindset, and driving digital innovation. Artificial intelligence (AI) will not replace people, but will support them by combining human creativity with the data processing power of AI to create innovative solutions. The future of clinical development lies in our ability to harness the power of data and AI while maintaining our human-centered approach. This requires an organizational enablement through data-driven stories, trainings, and coaching.

Rapid progress in genAI has enabled advanced structured content authoring of diverse clinical, regulatory, and safety documents. We created an enterprise-wide web application to autogenerate whole documents such as the Clinical Study Report, Informed Consent Form, and Product Safety Update Report using an extensible content template. The application can create whole documents in minutes by synthesizing and adding each section according to the template including AI-generated text, tables, images, and diagrams.

We used a semi-automated, GenAI-powered Protocol Schedule of Activities (SOA) table analyzer to evaluate clinical trial study burden. This tool provides study teams with competitor intelligence and promotes early discussions. It delivers actionable insights on patient burden related to protocol design, with the goal of minimizing protocol complexity and reducing the need for amendments.

The presentation will review where sponsors and CROs are deploying AI (artificial intelligence) and machine learning (ML) in clinical development based on a collaborative industry study. The presentation will examine the challenges of implementation as well as those methods that can increase trust in AI/ML across organizations.

In our modern world everything we do relies on advanced technology and computational systems that are allowing us to answer ever more complex questions and develop ever more effective therapies. Our collective advancement depends upon our shared understanding of the driving factors in Science and Medicine. The ability to coalesce our knowledge across domains and business functions via categorical language used in Industry standards allows us to meet urgent and emerging patient needs. Data that is ‘born FAIR’ or that is harmonized using FAIR principals elevates our data and enables more of our data assets to drive insights along the Drug Development pipeline. Unitized discrete annotation using ontologies integrated in systems supports FAIR data, enables analytics ready data and accelerates insights. Designing an integrated data ecosystem that is connected via a purposefully engineered semantic layer allows our computational systems to ‘understand’ the shared meaning embedded in data and documents along the pipeline, across domains, and through time.

The IEEE-SA CTTMN will discuss its work on five crucial technical standards to digitize clinical research: AI/ML, Risk-Based Assessment, Telehealth, Cybersecurity, and Hospitals at Home. These standards ensure consistency, reliability, and interoperability, enhancing patient safety, data quality, and security. By addressing key areas, they pave the way for efficient, secure, and patient-centric trials. Join this talk to discover how these groundbreaking standards will reshape clinical research and elevate your trials.

ICH E6 R3 states that Audit Trail needs to be reviewed regularly very few organizations have robust ways to do this work. These are huge files and not easy to analyze. We will discuss best practice for prioritizing data checks, automating data flow, developing analysis ready data sets and a roadmap for how to implement this work in your environment.  

As clinical trials increasingly adopt decentralized clinical trial (DCT) solutions to improve patient accessibility, the Tufts CSDD-PACT Consortium has collected and analyzed data from 69 trials to assess the effectiveness of various DCT methodologies. This presentation reveals key insights into the most frequently used DCT solutions and their impact on recruitment, retention, and cycle times, emphasizing the need for ongoing data collection to optimize DCT implementation.

As AI and machine learning transform biopharma R&D, building a scalable, cost-efficient, and compliant cloud infrastructure is essential for accelerating innovation. We have embraced a fully cloud-native approach to power AI/ML workloads, enabling seamless access to data, high-performance compute, and secure collaboration. This session will explore key architectural decisions, trade-offs considered, and best practices for optimizing cloud environments to support AI/ML at scale. Attendees will gain insights into leveraging cloud technologies to drive scientific breakthroughs while maintaining flexibility, security, and cost efficiency.