Register Now: Beyond EDC: Designing a Connected Data Strategy for Predictive Clinical Development
Blog|Slideshows|October 24, 2024

4 Challenges With Technology in Clinical Trials

While recent advancements in technology are helping studies operate more efficiently, concerns around their accuracy and usability still remain.

The shift towards technology and other decentralized approaches in clinical trials has been occurring for years now. The promise of artificial intelligence (AI) in recent years has only accelerated this shift. While the most up-to-date advancements in technology are solving age-old in challenges in trial execution such as long timelines, patient recruitment, and high costs, there are levels of caution and responsibility that must be taken.

In the current landscape, it seems as if there is a solution for almost everything. A number of technology vendors have entered the research space. Stakeholders that are looking to integrate these technologies must have a multi-faceted approach to choosing which are best for their specific studies. There are also regulatory guidelines that must be adhered to.

Over the past year, Applied Clinical Trials has spoken to several industry leaders on the topic of technology in clinical research. In this slideshow, Gareth Dabbs, vice president, global product strategy, IQVIA; Sam Liu, VP of marketing, Vivalink, Ginny Beakes-Read, head, global regulatory policy and intelligence, Johnson & Johnson Innovative Medicine; and Rob DiCicco, vice president, portfolio management, TransCelerate Biopharma share some of their concerns with clinical technology and how they can be addressed.

Image Credit: © Blue Planet Studio - stock.adobe.com


Related to this article

Building AI Assistants Clinical Trial Teams Can Trust: Q&A with E.K. Koh, Suvoda
In this Q&A, E.K. Koh, chief product officer at Suvoda, discusses what separates a trustworthy AI assistant from one that adds risk, why curated clinical knowledge matters more than general-purpose flexibility, and what safeguards must be in place when assistants move from answering questions to taking actions in clinical trial systems.
© photon_photo - © photon_photo - stock.adobe.com
Standardized measurement across five oncology centers found that clinical research coordinators rated eSource-enabled workflows positively (Overall Mean Satisfaction 5.79 of 7), with reduced manual entry and electronic data transfer earning strong marks, while perceived reduction in sponsor and monitor queries lagged as the one domain below threshold.
Where the Industry Is Headed as Clinical, Operational, and Real-World Data Connect
In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, describes the industry’s trajectory toward a unified intelligence orchestration environment—and explains why the future isn’t one model that knows everything, but a trusted system that brings the right evidence to the right human decision.
What Expanding AI Ecosystems Mean for Sponsor Complexity and Oversight
In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, argues that CROs must act as orchestrators rather than aggregators—and that the real test of any AI ecosystem is whether it reduces sponsor coordination burden or just hands it back to them.