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Artificial Intelligence in Clinical Research

Artificial intelligence (AI) and machine learning (ML) have propelled many industries toward a new, highly functional and powerful state.

Trial Analytics, Bristol-Myers Squibb In the past years, BMS dedicated tremendous efforts to explore and implement novel analytics solutions to advance drug development and deliver innovative products that show unprecedented features.

This talk will examine the factors why machine learning techniques are getting traction in the life science industry, which include what machine learning approaches are useful in life sciences, new data dynamics e.g.

and easier access to data sources which can give greater insight into products and new diagnostics/evaluation techniques.

can improve trial protocols, design, and execution and how AI provides novel ways to monitor and diagnose patients using digital technology.

Pharmacovigilance, Astellas Given the wide variety of global regulatory requirements, managing the volume, variety and velocity of Pharmacovigilance data presents a significant challenge.

TransCelerate’s newest Intelligent Automation initiative focuses on identifying how intelligent automation technologies can be used to better support execution of Pharmacovigilance

By conducting an impact assessment and working with global health authorities to verify risks/issues with their use, this initiative will provide guidance, as appropriate, on applications of new technology in Pharmacovigilance

Officer BI X GmbH, Boehringer Ingelheim The real world data (RWD) hype caused high expectations, including how RCTs might only play a minor role in future drug development.

The same is true for artificial intelligence: AI is a tool applicable in different stages of drug development, supporting RCTs as well as RWD studies to generate evidence.

Centre of Excellence, GSK Factors that cause clinical trials to fail their primary endpoints can be difficult to uncover with traditional statistical methods.

Insights on features that drive primary endpoint failure can be used to inform better clinical trial study design in the future.

9:55 Practical Applications of Natural Language Processing Malaikannan Sankarasubbu, Vice President, AI Research, SaamaTechnologies 10:25 Networking Coffee Break (Sponsorship Opportunity Available) RPA IN CLINICAL TRIALS 11:10 Chairperson’s Remarks Chairperson to be Announced 11:15 The Use of RPA (Robotic Process Automation) within Data Management at Novartis Sarah Clark, BSc, Stats and Computing, Global Head, Data Management, Novartis As the digital age progresses, how are companies using technology to increase throughput and reduce/eliminate monotonous tasks?

a solution which enforces rules, privacy, and regulations in a mutually agreed upon manner, resulting in a smart-contract between

Associates The most significant costs to clinical trials are in time and resources to insure the com-pleteness, accuracy and integrity of patient data.

Can blockchain solutions be applied to reduce the time to bring new biopharmaceu-tical products to market while reducing the cost of achieving that objective?