Artificial Intelligence/Machine Learning

An increase in protocol complexity is paving the way for machine learning models to optimize trial design.

Leveraging artificial intelligence-powered stochastic methods for clinical data review represents a significant leap beyond traditional approaches.

Understanding the benefits and concerns associated with implementing datasets into clinical trial workflows.

COVID-19 not only advanced scientific boundaries, but also transformed research methodologies and accelerated adaptive clinical trial design.

In this video interview, Cameron Breze, product manager, Inovalon, discusses the need for accurate information in creating useful clinical data.

Genomics has long been a primary focus in precision medicine, but other areas of research are increasingly being used to help attain a more complete picture of how an individual’s complex biology impacts their health profile.

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.

In an interview with Applied Clinical Trials Associate Editor Don Tracy, Shakthi Kumar, chief strategy & business officer, Edetek, provides examples of how Agentic AI can accelerate clinical development.

In this video interview, Dominique Demolle, CEO of Cognivia, talks artificial intelligence/machine learning and its potential in gathering patient data.

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

In an interview with ACT senior editor Andy Studna at SCOPE Summit, Rajneesh Patil, vice president, digital innovation, IQVIA, highlights the impact of artificial intelligence/machine learning in improving outcomes and maintaining safety with its implementation.

In an interview with ACT senior editor Andy Studna at SCOPE Summit, Patil, vice president, digital innovation, IQVIA, discusses how artificial intelligence/machine learning can help in areas such as feasibility, site selection, and patient recruitment.

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.