Data Science, AI and Digital Innovation

The data revolution is reshaping every stage of pharmaceutical R&D and operations—from hypothesis generation to post-market learning. Data Science, AI & Digital Innovation convenes scientists, clinicians, statisticians, and technologists to build trustworthy, regulation-ready digital capabilities that accelerate discovery and improve outcomes. This session explains how modern data architectures connect ELN/LIMS, clinical data platforms, and real-world evidence pipelines to create a single source of truth for decisions. It highlights practical AI applications that already work—de novo design, image analysis, and predictive maintenance—while clarifying model governance, documentation, and validation requirements so innovations survive audits and scale across programs. Participants will see how foundation models and domain-specific machine learning deliver value when paired with strong metadata, ontologies, and versioned datasets that make results reproducible and secure.

A core theme is “GxP by design”: ensuring algorithm lifecycle control, explainability, and change management so models remain fit-for-purpose under evolving data and regulations. We explore human-in-the-loop patterns that combine expert judgment with automation, reducing cycle times without compromising scientific rigor. The session also examines digital twins for process and patient: mechanistic–statistical hybrids that simulate manufacturing states, project clinical trajectories, and guide dose selection or process adjustments before risks materialize. Attendees will learn how to stand up FAIR data ecosystems, deploy privacy-preserving analytics, and operationalize dashboards that deliver insights to chemists, clinicians, and quality leaders at the moment of decision.

On the clinical side, we discuss AI-enhanced endpoint detection, sensor analytics, and data fusion across ePRO/eCOA, imaging, and labs; in manufacturing, we cover PAT, anomaly detection, and soft sensors that anticipate drift. Finally, we address culture and skills—how cross-functional teams adopt modern MLOps, data engineering, and statistical quality control to convert pilots into durable capabilities. For professionals seeking a Pharma Conference, this track provides implementation blueprints, inspection-ready documentation patterns, and case studies where digital systems demonstrably reduced cost, shortened timelines, and improved benefit–risk. Foundational machine learning principles are woven throughout to ensure methods are transparent, validated, and transferable across products and sites.