Artificial Intelligence (AI) has been continuously integrated into the field of clinical research. A formerly time-consuming workflow has now been shifted into an efficient process with lowered cost, less labor and improved clinical trial outcomes. As society shifts into a technology and digital driven era, it is important to see how this can be leveraged within clinical trials. We will take a look at 3 prominent ways that AI has been streamlining the clinical trial process since the shift into the digital age.  AI in clinical trials will continue to become a dominant theme among clinical technology and strategy professionals for the foreseeable future.

1 Recruitment

Subject recruitment within clinical trials is considered one of the most crucial determinants for a successful trial. There are many challenges faced in this area that can lead to failure in reaching recruitment goals and inaccurately recruiting the proper subject for the study protocol. Minimizing recruitment barriers is pertinent, therefore, this is where AI comes into play. Considerable efforts are put forth towards recruitment. For example, sites typically assess eligibility by conducting interviews, thorough EMR reviews, physical exams, calling potential patients, numerous outreach events, etc. which directly affects the amount of paperwork, employees needed, and clinic time to carry out this process. AI can be implemented to analyze large databases leading to more efficient and reliable processes and eliminate these common recruitment limitations¹. Defined inclusion and exclusion criteria, demographics, imaging parameters, and comorbidities can be identified and included in database searches performed by AI. AI is a trained system that can extract those ideal patients within an EMR system or other recruitment databases and match them with complex clinical trial criteria while minimizing the common risks faced within recruitment. Eligibility is validated, as well as the ability to predict patient retention through AI proving promising results for clinical research.

2 Data Collection

To produce results of drug efficacy and safety for eventual usage, the collection, cleaning, and management of high-quality data is necessary in the field of clinical research.  One way that AI is streamlining data collection in clinical trials is through the use of digital health technologies (DHTs). By relying on AI algorithms, automated data collection produces usable, real-time information through wearable devices, sensors, investigational product trackers, video capture, etc.² These features allow a site or sponsor to prioritize the safety of subjects, while obtaining actionable insights through data. Additionally, defining of biomarkers while continuously collecting data through AI, can validate patient drug responses, identify sudden changes, or predict patient health outcomes for the study.

3 Predictive Insights

Another key indicator for a successful clinical trial is proper study design. AI is being utilized to enhance the overall study design process through the prediction of trends in patient data, success rates, and outcomes, which leads to a reduction of the length and cost of a trial. The success rate of a trial can be predicted by AI through previous patterns, patient data, site specific data and related trials. Within patient outcome prediction, it is noted that AI is being used to simulate data that allows for a more efficient statistical outcome measure and identify patients who are progressing to reach endpoints quicker, which results in shorter trial durations². Predictive insights allow for sponsors, clinical research organizations, and research sites to make informed decisions on what trials are best suited for their needs. The risk of failure, time, and resources are reduced with this information and allow for transparency on the expected future of the trial. Additionally, this allows for design teams to make improvements upon the trial with the predictive insights provided.

AI in clinical trials is expected to continually be incorporated into the field of pharmaceutical and biotech research. The streamlining of the processes within clinical trials will be evolving over time with the help of AI. Innovation will continue to challenge the field and help grow in areas that were once unheard of. While there will be challenges that come alongside AI integration, the benefits are undeniable within clinical research and significant strides will be made towards enhancing their processes.

About Astrix

Astrix is the unrivaled market-leader in creating & delivering innovative strategies, solutions, and people to the life science community.  Through world class people, process, and technology, Astrix works with clients to fundamentally improve business & scientific outcomes and the quality of life everywhere. Founded by scientists to solve the unique challenges of the life science community, Astrix offers a growing array of strategic, technical, and staffing services designed to deliver value to clients across
their organizations.

References

1. Ismail A, Al-Zoubi T, El Naqa I, Saeed H. The role of artificial intelligence in hastening time to recruitment in clinical trials. BJR Open. 2023;5(1). doi: https://doi.org/10.1259/bjro.20220023.

2. Askin S, Burkhalter D, Calado G, El Dakrouni S. Artificial Intelligence Applied to Clinical trials: Opportunities and Challenges.Health Technol. Published online February 28, 2023. doi: https://doi.org/10.1007/s12553-023-00738-2.