AI-Driven Transformation: How SCDM and the Industry Are Walking the Talk

Patrick Nadolny has over 30 years of industry experience across pharmaceutical, device and biologics as well as technology development. He is a pragmatic leader focusing on technology, innovation, strategic planning, change management, and the setup of new capabilities. In addition to his SCDM Chair role, he leads the SCDM innovation committee, which recently released 3 reflection papers on the evolution of Clinical Data Management toward Clinical Data Science.

Maciej has started his Data Management career as a Data Manager in Pharmacovigilance department. After few years, he pivoted into Data Analysis and then into AI / NLP environment. Currently, Maciej is working with Generative AI and its' utilization in professional setting, trying to close the gap between technology and business.

Dr Deepak brings in over 18+ years of experience in Industry with high expertise for Project’s, Program’s and Portfolio’s handling for various FSP’s and Stand-alone services. In his current role is responsible for Governance of FSPs, building technology alliances and Operational Delivery at Global Level. He is a Certified Clinical Data Manager and Certified Process Manager. Has participated in various SCDM, ISCR, PhuSE and DIA conferences as Speaker, Session Chair, Co-chair , Chair for SCDM certification committee and Board of Trustee at SCDM.

Prasanna Rao is an AI practitioner and Industry Thought Leader whose current role is the Head of Artificial Intelligence and Data Science for Data Management and Monitoring at Pfizer. He has over 25 years of experience in Information technology and Analytics, with 10 years in Healthcare and Life Sciences. He has implemented several AI projects in the last 8 years including projects with IBM Watson Health, as a Watson Solution Architect. His role at Pfizer is to analyze Clinical Development use cases, carefully evaluate various AI/ML techniques, conduct hackathons, and implement AI & automation solutions to deliver business value.