Healthcare Technology Featured Article

September 15, 2022

How the Use of AI will Improve Clinical Trials


The Art in the Trial

In recent years, the life sciences have made a dramatic leap into the digital age. As a result, AI is recognized for its potential to reduce costs and accelerate every stage of clinical research and drug development, from matching patients with a clinical trial and handling data to discovering drugs.

Researchers and the pharmaceutical industry are using artificial intelligence to improve clinical trials (AI). Sophisticated machine-learning algorithms, fueled by the rapidly increasing amounts of medical data available to researchers, including that provided by electronic health records and wearable devices, have the potential to save billions of dollars, accelerate medical advances, and expand access to experimental treatments. Here are the main areas in which AI is helping aid the clinical trial process.

Patient Identification

While patient selection and recruitment can be time- and resource-intensive, a successful determination of participants increases the trial's efficacy potential. However, unsuccessful cohort determination and recruitment are among the most costly monetary burdens. If trial recruitment is not successful and timely, the trial may fail. This potential financial loss should be viewed as an incentive to use artificial intelligence (AI) technologies early in clinical trials.

AI technology can sift through massive amounts of data to identify patient subgroups who may benefit more from a clinical trial. It can also analyze social media content to identify specific regions where a condition is more common, narrowing the search for the appropriate cohort. By analyzing hospital medical records and alerting both clinicians and patients about clinical trial opportunities, AI has the potential to speed up the process of finding eligible participants. Technology can also help to simplify complex entry criteria and make them more appealing to potential candidates.

Trial Design

Biopharma firms are using a variety of strategies to innovate trial designs. Excessive scientific and research data, such as current and previous clinical trials, patient support programs, and post-market surveillance, have energized trial design. With their unrivaled ability to collect, organize, and analyze the growing body of data generated by clinical trials, including failed ones, AI-enabled technologies can extract meaningful patterns of information to aid in design.

Efficiency

As therapeutic compounds approach human trials, machine learning can help maximize trial success and efficiency during the planning phase by applying simulation techniques to large amounts of data from previous tests to facilitate trial protocol development. For example, as demonstrated in reinforcement learning approaches to Alzheimer's disease and non-small cell lung cancer, study simulation may optimize the choice of treatment regimens for testing. In addition, AI allows investigators to upload protocols and uses natural language processing to identify potential pitfalls and roadblocks to trial completion.

Study Monitoring

By collecting and analyzing data in real-time, artificial intelligence can assist a company in monitoring a clinical trial. These AI solutions can gather a large amount of data from various sources, allowing doctors or researchers to understand better how patients react during a drug trial. Clinical research provides a wealth of operational data, but multiple applications and functional data silos prevent pharma executives from getting a complete picture of their clinical trials portfolio.

As a result, hours are lost each day attempting to optimize trial operations and improve cost and resource efficacy by collecting and analyzing various data sets. Pharmaceutical companies can better determine whether a data anomaly is a genuine risk by combining operational data from clinical trials with AI and advanced predictive capabilities on an analytics platform, allowing for more efficient and successful visits.

Cost Savings

Artificial intelligence can also assist researchers in fine-tuning their trials before they are launched. This can help businesses save money while increasing the likelihood of trial success. Every clinical trial follows a protocol that details how the study will be carried out. Any issues that arise during the trial that necessitate protocol changes can cause months of delays and cost hundreds of thousands of dollars.

Conclusion

Life sciences organizations can use AI to transition from large teams working across dozens of data systems to a single AI-enabled, standards- and metadata-driven backbone that requires little user input. As a result, AI-enabled data collection and management can accelerate the drug development process and help companies bring new treatments to market more quickly by reducing the time and effort required for clinical trials.









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