AI in Clinical Trials

Starting from predictive analytics introduced in 2017, we have continually expanded our AI capabilities. By 2020, we were leveraging Machine Learning for predictive analytics across all clinical research metrics, providing precise and comprehensive insights.

In 2023, we introduced “Ask AI” for clinical risk assessment and AI-augmented regulatory inspections, enabling targeted and efficient risk assessments and regulatory preparations.

As we celebrate our 10th anniversary in 2024, we are proud to launch AI-augmented clinical Due Diligence, further enhancing our suite of AI-driven solutions.

Mar 2024

Key Takeaways from PHUSE US Connect 2024

By |2024-05-10T17:03:25+02:00March 5, 2024|AI in Clinical Trials, Blog, News|Comments Off on Key Takeaways from PHUSE US Connect 2024

Explore key takeaways from the PHUSE US Connect 2024 conference, highlighting the future of clinical trials, the importance of data integrity, and the role of AI/ML methodologies. Discover how Cyntegrity's vision for QbD and integrated RBQM is shaping the industry.

Nov 2023

Cyntegrity Hosts Virtual Dialogue on Responsible AI in Clinical Trials

By |2024-05-10T17:00:40+02:00November 2, 2023|AI in Clinical Trials, News|Comments Off on Cyntegrity Hosts Virtual Dialogue on Responsible AI in Clinical Trials

We proudly present our virtual dialogue on Responsible AI in Clinical Trials this November 15 & 16, uniting industry and academic experts for groundbreaking discussions and insights.

May 2020

AI-Enhanced Machine Learning Predictions for All Clinical Research Metrics

By |2024-05-10T17:02:08+02:00May 27, 2020|AI in Clinical Trials, Neat Features, News|Comments Off on AI-Enhanced Machine Learning Predictions for All Clinical Research Metrics

MyRBQM® Portal's new Machine Learning predictive analytics model is far less sensitive to noise, it better generalizes data, and provides narrower CIs in long time series. Singular Spectrum Analysis is particularly valuable for long time series, as mostly observed in clinical trials.

Aug 2017

AI-Driven Predictive Analytics in Risk-Based Monitoring – Part II

By |2024-05-10T17:05:52+02:00August 8, 2017|AI in Clinical Trials, Blog, Neat Features|Comments Off on AI-Driven Predictive Analytics in Risk-Based Monitoring – Part II

In our previous "AI-driven predictive analytics in RBM" article, we started a discussion about algorithms of machine learning (ML), predictive analytics, and artificial intelligence (AI). We also covered that a risk software needs to calculate forecasts of Key Risk Indicators (KRIs) proactively and alerts [...]

Aug 2017

AI-Driven Predictive Analytics in Risk-Based Monitoring – Part I

By |2024-05-10T17:06:46+02:00August 2, 2017|AI in Clinical Trials, Blog, Neat Features|Comments Off on AI-Driven Predictive Analytics in Risk-Based Monitoring – Part I

AI-driven Predictive Analytics is a very useful tool in risk-based monitoring and overall risk-based study management. It increases the proportion of correct decisions once the decisions start to become more data-driven. It also helps to understand for a central CRA or study manager the [...]

Go to Top