Aardvark released exploratory data from its stopped Phase 3 HERO trial in Prader-Willi syndrome after FDA lifted the hold, and plans a redesigned Phase 3.
|Webcasts|May 26, 2020
Using Artificial Intelligence to Improve Clinical Trial Efficiency and Optimization
Machine learning technologies can help predict outcomes in clinical trials, leading to faster drug approval times, lower costs, and more funding to develop new treatments. How can machine learning deliver business intelligence in the initialization of trials? Live: Tuesday, May 26, 2020 at 1pm EDT | 12pm CDT | 10am PDT On demand available after airing until May 26, 2021. Register free
Register free:
Event Overview:
The complexity of initiating studies continues to grow, a confluence of complicated protocols, globalization, and regulatory changes, at a time when there is intense pressure to speed clinical trials and restrain costs. Key to reducing these complexities is the ability to be able to leverage operational insights with granular performance metrics and machine learning, which can guide clinical research staff in their daily activities.
Machine learning technologies can help predict outcomes in clinical trials, leading to faster drug approval times, lower costs, and more funding to develop new treatments. More accurate predictions can reduce the uncertainty in study execution by providing greater risk transparency and allowing informed data-driven decisions to be made in the risk assessment and portfolio management of investigational drugs in clinical trials.
How can machine learning deliver business intelligence before starting studies?
Key Learning Objectives:
In this webcast we will explore:
- What is required in order to make machine learning a reality?
- The applicability of machine learning to make more precise predictions in planning studies now
- How machine learning can be leveraged now to improve daily activities for clinical research staff
- Where machine learning/artificial intelligence can provide the most value:
- Impact on study design: traditional site-based trials vs. decentralized trials?
- Impact on country selection?
- Impact on KPIs: balancing enrollment vs. speed?
Speakers: Jeff Kasher, President, Patients Can’t Wait
Moe Alsumidaie, Head of Research, CliniBiz
Rob Scott, Chief Medical Officer and VP of Development, AbbVie
Elvin Thalund, Director, Industry Strategy, Oracle Health Sciences
Time and date: Tuesday, May 26, 2020 at 1pm EDT | 12pm CDT | 10am PDT
On demand available after airing until May 26, 2021.
Sponsor: Oracle Health Sciences
Register free:
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