Session 31 - Movers & Shakers of Clinical Data Management - Demystifying Novel Challenges & Building Trust in AI/ML Models

  • #pioneer
Wednesday, September 16, 2020: 11:15 AM - 12:00 PM
/ CEU Credits:0.2

SESSION CHAIRS & SPEAKERS

Speaker
Ian Shafer
Partner
PricewaterhouseCoopers
Session Chair
Mrunalini Jagtap
Data Management Lead
SICS, A*STAR
Speaker
Patrick Nadolny
Associate VP, Clinical Data Management and Programming
Allergan (an Abbvie company)
Speaker
Shobhit Shrotriya
India Lead, Life Sciences R&D Services, Accenture Applied Life Sciences Solutions
Accenture

SESSION DESCRIPTION 

As the AI/ML fields mature and sophisticated models get deployed across critical clinical data processes, new challenges come up. One of these of the paramount importance is the perception that these models are behaving as 'black boxes' i.e. it is difficult to understand how they work and the reasoning behind their predictions. This is coupled with increasing efforts on removing 'bias' i.e. discovering that the model uses discriminatory features in the prediction. Both of these lead to rising levels of mistrust in AI/ML systems. In this session, our eminent speakers will -

  • unveil the motivations behind the Interpretable, Explainable, Responsible and Ethical AI; 
  • untangle its impact on patients, sponsors & regulators; 
  • explore initiatives, tools & techniques to demystify these new challenges; 
  • enable to build trust in these state-of-the-art AI/ML models and deploy these in clinical trials so as to bring right treatments to the right patients at the right time.

TOPIC


LEARNING OBJECTIVES

• Attendees will benefit from case studies that will motivate them to adopt AI-ML in their organizations
• Attendees will understand the challenges in using AI-ML
• Attendees will also be able to see upcoming trends and changing regulatory landscape so as to reinvent their AI-ML strategy and become future ready for greater sustainability

SESSION INFORMATION

CDM Certification Competencies::
Project Management,Data Management Plans,SAE Reconciliation and Safety Review,Coding Medications,Communication of Data Trends,Coding (AEs; Signs and Symptoms),Analysis & Reporting

Target Audience:
Clinical Data Manager,Program Manager,Clinical Data Scientist,Data Transformation Lead,Change Agent