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1 min read

How to Manage Machine Learning Algorithms in Clinical Trials

As today’s clinical trials grow increasingly complex, risk-based monitoring has become the watchword of the day. It is integral to any quality management program; it is mandated by regulatory agencies; and, critically, it is a potent tool for forestalling costly failures…

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Six Considerations When Implementing Machine Learning Algorithms in Clinical Research

Machine learning provides a crucial opportunity to shape and direct clinical trial research, helping harmonize and analyze the massive amounts of data generated in the course of a study, providing the means needed by researchers to make faster, more informed decisions. Yet, the machines are only as effective as their algorithms and their interaction with

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Age of the Machine: Using Algorithms to Advance Outcomes

Starting with trial design and site selection, machine learning algorithms can be used at every stage of drug development in order to increase efficiency and data quality. It offers endless opportunities for medical imaging, trial recruitment, and other aspects of the life-sciences industry.

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Swimming in Data

By: Kristin Mauri and Rhonda Roberts at Remarque Systems The amount of data collected during clinical trials has increased dramatically. In order to keep up with trends, machine learning offers the opportunity to keep studies on the right trajectory using real-time data to identify anomalies and missing data. This is important not only during the

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How machine learning and artificial intelligence can drive clinical innovation

As the global pandemic has created a greater need for remote monitoring, the adoption of machine learning and artificial intelligence (AI) into clinical trials can help to generate new efficiencies to enhancing patient safety and trial outcomes, while not replacing the need for clinical trial monitors or other research personnel. This Byline explores the links