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1 min read

Data Literacy: The Foundation for Modern Trial Execution

Data literacy is a simple concept. Looking at data related to your area of specialty, you can deduce insights, ask appropriate questions, and make clear data-based decisions. Within the clinical trial ecosphere, each role requires a different degree of skill both vis-à-vis data in general and the trial data specifically.

1 min read

Clinical Trial Evolution: The Drive to Update ICH E6

The ICH E6 guidelines has set the clinical trial industry standard since it was first published in 1996. As clinical trials and technologies, however, continue to rapidly evolve, there is a need for these guidelines to shift as well. The industry has called for a revision of the ICH E6 (R2) guidelines to spotlight risk-based

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…

1 min read

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

1 min read

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.

1 min read

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