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AI & Machine Learning for Clinical Trial and R&D Advancements 2018

Thu, June 14 - Fri, June 15, 2018 Philadelphia, Pennsylvania
Philadelphia, Pennsylvania United States

About AI & Machine Learning for Clinical Trial and R&D Advancements 2018

AI & MACHINE LEARNING FOR CLINICAL TRIAL AND R&D ADVANCEMENTS, PHILADELPHIA, JUNE 14-15, 2018

Artificial intelligence is now recognized as a powerful tool for enhancing research and development operations. Innovations in machine learning have the ability to analyze large volumes of data for better insights into diseases and drug development. While applications, systems, and platforms have been developed to transform clinical trial innovation, there has not been a standard for benchmarking in AI’s actual execution.

At the AI & Machine Learning for Clinical Trial and R&D Advancements event, our high-caliber speaking faculty of machine learning and drug development leaders provides key insights into the AI vertical use in clinical trials. Attendees will walk away with the ability to successfully implement AI platforms to advance drug discovery, streamline clinical trials, and coordinate site-sponsor activities.

Who Should Attend

This conference is designed for professionals from pharmaceutical, biotechnology and medical device companies with responsibilities in:

  • Artificial Intelligence
  • Data Science/Management
  • Clinical operations
  • Medical Imaging
  • Clinical Research/Development
  • Genomic Profiling
  • Personalized Medicine
  • Drug Development
  • Innovation
  • Bioinformatics       
  • Computational Systems
  • Advanced Analytics
  • Modeling Platforms
  • Machine Learning
  • Drug Development
  • Deep Learning
  • Business Development
  • Strategic Partnerships
  • Innovation
  • Automated Intelligence

Top Five Reasons to Attend

  • Explore the future applications of AI and machine learning in new era of drug discovery and development
  • Review applications of deep learning to biomarker development
  • Review the state of AI currently being applied to drug discovery through case studies
  • Analyze relevant regulatory frameworks that govern workflow and reporting obligations in the life sciences industry
  • Determine how frequently models needed to be updated to better support drug discovery projects

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