Eight Approaches to Leveraging AI in Clinical Trials with the Key Industry Innovators

In the ongoing pursuit of new drugs and medical treatments, the pharmaceutical sector faces a complex task: verifying the safety and efficacy of these potential advancements. Clinical trials, long-drawn, expensive, and fraught with uncertainty, act as the proving ground for these potential solutions. To overcome these challenges, pharma R&D teams are increasingly leveraging the power of artificial intelligence (AI) in clinical trials with the goal of improving efficiency and reducing costs.

The complexity of drug development is evidenced by the high proportion of R&D endeavors that do not result in a market-ready product. Even when a potential drug reaches the clinical trial stage, the path is strewn with hurdles - a mere 12% secure approval from the U.S. Food and Drug Administration. Given these odds, pharma companies are increasingly relying on AI's capabilities to augment this percentage without compromising safety.

The convergence of AI and clinical trials holds the promise of not just expediting the drug discovery process but also enhancing the caliber and efficacy of the treatments that are developed. For instance, AI's potential to increase the accuracy of the initial selection of compounds could drastically boost the success rates of clinical trials. This potential shift holds the promise of ushering the pharmaceutical industry into a new epoch marked by heightened efficiency and innovation.

By using AI-driven algorithms for a wide range of tasks, from data entry and analysis to improved medical diagnoses, companies can reduce the time and cost associated with drug development. Here are seven ways companies are using AI in clinical trials.

1. Patient Recruitment and Screening

Clinical researchers are leveraging AI technologies to improve patient recruitment and screening processes. AI algorithms excel at analyzing vast volumes of patient data to swiftly pinpoint potential trial participants based on predefined inclusion and exclusion criteria. Furthermore, AI-powered chatbots and virtual assistants are equipped to disseminate trial-related information, respond to patient inquiries, and gather preliminary data. The result is a reduction in the time and resources devoted to manual patient screening and interviewing.

Based in the United States and established in 2010, AICure has carved out a reputation for its innovative healthcare solutions. The firm's flagship offering, AiCure Patient Connect™, comprises a suite of HIPAA and GDPR-compliant tools encapsulated within a mobile application. This app is designed to boost patient engagement, enhance the site-patient relationship, and foster a deeper understanding of both individual and population-wide disease symptomatology, with the goal of improving health and trial outcomes. The company's AiCure Data Intelligence platform provides a highly customizable data ingestion and visualization service. This feature grants sponsors access to real-time and predictive insights, thereby allowing advanced visibility into the performance of individual trials and sites.

Additionally, AICure's AI platform as a service (PaaS) enables the dynamic aggregation of disparate data sources. This allows for the correlation of previously unrelated endpoints and their translation into meaningful, actionable insights that can be deployed at scale. AICure's innovative approach to AI in clinical trials has garnered $52.8 million in funding from several investors, including Palisades Growth Capital.