The use of machine learning and artificial intelligence in society has been one of the most significant technological achievements of the 21st century. While basic AI systems were used in the late 20th century, the development of processes and systems that use ML and other functions has exponentially increased over the past decade. These technologies allow researchers across countless fields to better manage data and scale their efforts.
AI and ML continue to be used in diverse ways in clinical trials. Here are a few key ways machine learning can change how you develop trials and test their feasibility, helping your organization save time and money.
One of the best parts of AI tools is that they aren’t limited to one aspect of the clinical trial. Teams can tap into data sources in the early stages of trial development to quickly see which trials have already been completed. They can review the results of those trials and make adjustments to their own plans.
“Various ML approaches are available for managing large and heterogeneous sources of data, identifying intricate and occult patterns, and predicting complex outcomes,” write researchers E. Hope Weissler, et al. “As a result, ML has value to add across the spectrum of clinical trials, from preclinical drug discovery to pre-trial planning through study execution to data management and analysis.”
The use of machine learning and artificial intelligence continues to be applied throughout trials, through to the final review before publication.
One of the top benefits of machine learning in clinical trials is the use of third-party data from previous trials and outside sources. There are hundreds of thousands of trials taking place across the globe, many of which have similar goals and processes. With the right ML tools, teams can identify other trials that might have valuable data for cross-sharing and integration.
This is where a tool like TA Scan by Anju comes in. This system collects, aggregates and analyzes data sources and presents the information in one place. It allows researchers to quickly review other trials and find data to incorporate into their own studies.
As more researchers identify data they can use, new problems arise. One issue that many researchers face is the creation of data silos. Teams will have multiple data sources that are difficult to compare because the data lives in different places and is presented in different manners.
“If you have a small amount of data from two variables, it is easy to plug that information into a spreadsheet, graph it, and look at the relationship between the variables,” writes Ed Miseta, chief editor at Clinical Leader. “That process becomes more complicated when you have multiple variables and millions of data points.”
By working with an AI system that brings data sets together, teams can better compare and contrast data in one place.
Artificial intelligence also has the power to help researchers adjust their trials before they launch them. This can help companies save money while increasing the chances of trial success.
“Every clinical trial follows a protocol that describes exactly how the study will be run,” says science writer Marcus Woo at Nature. “Any problems that arise during the trial and that require amendments to the protocol can lead to months of delays and add hundreds of thousands of dollars to the cost.”
Anju’s TA Scan has a Trial Feasibility Wizard which reviews historical public clinical trials against your trial plans. This allows for more accurate planning of clinical trials. Not only can third-party sources be used in your data, but researchers can also use them to learn from the mistakes of others.
Artificial intelligence and machine learning are assets to your internal research operations, contract research organization partners and labs. They are tools to guide your trial development, improve your data and provide checks to ensure accuracy and clarity.
By incorporating these resources into your research processes, you can increase your trial success rates and help other scientists across the globe. Everyone benefits when clinical trial data is easier to share, understand and use.
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