The Impact of Artificial Intelligence in Clinical Trial Recruitment

Artificial Intelligence (AI) is a field with a long history and which is still actively evolving and changing. Until now, we have seen the basics of modern AI, as well as some of the typical applications of AI. Along the way, we also hope to see numerous applications and vast possibilities in the field of AI, which continues to increase human capability beyond our imagination.

Let us see how Artificial intelligence is lowering trial cycle times while increasing the costs of productivity and results of clinical development.

Transformation in clinical trials using artificial intelligence

TRADITIONAL 'linear and sequential' clinical trials remain the same to make sure that the efficacy and safety of new medicines are kept unchanged. However, the tedious tried and tested process of discrete and fixed aspects of randomized controlled trials (RCTs) was built mainly for mass-market testing drugs has changed a little in recent years. The traditional RCTs lack the analytical power, fluidity, and speed required to develop sophisticated new therapies that focus on smaller and often diversified patient populations.

The implementation of AI technologies is, therefore, becoming a vital business imperative in the following areas:

AI technology in clinical trial design

Many pharma companies are implementing a range of AI strategies to innovate trial design. Increasing amounts of scientific and research data, such as present and past clinical trials, patient support programs, and post-market surveillance, have enhanced trial design. AI-powered technologies, having the unparalleled power to collect, organize, and analyze the enormous data generated by clinical trials, including failed ones, can extract the right patterns of information to help with the design.

Patient enrichment, recruitment, and enrolment with Artificial intelligence

AI-powered digital transformation can enhance patient selection and improve clinical trial effectiveness through extensive mining, analysis, and interpretation of multiple data sources. This also includes medical imaging, electronic health records (EHRs), and 'omics' data. The FDA has published guidance that identifies mainly three strategies to help the biopharma industry and improve patient selection to optimize a drug's effectiveness, all of which can be possible with the help of AI technologies.

Artificial intelligence-powered Operational data to drive clinical trial analytics.

Trials create a humongous amount of operational data. Still, functional data cribs and different systems can prevent companies from having a broader view of their clinical trial portfolio over multiple global sites. Combining all data, regardless of the source on a shared analytics platform, backed by open data standards, can promote collaboration and integration and produce insights across essential metrics. Consolidating a self-learning system, intended to enhance predictions and prescriptions over time, together with data visualization tools, can proactively deliver reliable analytics insights to users.

While artificial intelligence technology is yet to be widely implemented and to clinical trials, it has the power to transform clinical development. Several applications of AI can offer faster, safer, and significantly less expensive clinical trials. The potential of AI to enhance the patient experience will also help achieve the purpose of biopharma to embed patient-centricity more adequately across the whole R&D process.

Ultimately, transforming clinical trials will require firms to work completely creative, drawing on change management skills, as well as communities and collaborations. If companies supersede in capitalizing on AI's potential, the productivity challenges can be declined, and active clinical trials can be achieved.