Artificial Intelligence/Machine Learning

Teams must work together with technology solutions and optimize integration to unlock their full potential.

Current industry trends align with the need for improved, fit for purpose technology.

A tool-based approach can significantly aid in the harmonization of regulatory requirements, mitigate inadvertent risks, and encourage collaboration by making data sharing more efficient and secure.

How targeted AI can improve the performance of clinical trials.

Machine learning can help investors dive deeper into trial data to evaluate the true potential of an asset and uncover new hidden opportunities.

In this video interview, Dipanwita Das, CEO & co-founder at Sorcero, highlights how artificial intelligence, real-time monitoring, and historical data can aid in optimizing trial design.

In this video interview, Kimberly Tableman, founder & CEO, ESPERO, highlights how data standards are empowering the use of artificial intelligence.

In this video interview, Kimberly Tableman, founder & CEO, ESPERO, talks protocol submission formats and how data interoperability can support artificial intelligence.

Unlocking the full potential of artificial intelligence requires these stakeholders to ensure their data are accessible and secure.

A well-designed approach can benefit clinical trials from protocol design to site support.

In this video interview, Jeff Sidell, PhD, chief technology officer, Advarra, talks AI and how it can automate repetitive processes.

The potential of next-generation platforms in transforming patient recruitment.

It takes a village to raise a trial, but most stakeholders are siloed on isolated islands.

Despite limitations to its widespread use within healthcare, there is great potential for ChatGPT’s application in drug development.

The FDA has taken a clear position with Project Optimus in shifting toward more progressive tailored approaches while rejecting antiquated study designs to evolve clinical trial strategies to better align with newer drug classes.

Findings suggests implementation could save time and money, but needs to be evaluated carefully.