AI-driven patient identification from electronic health records (EHRs) and real-world data.
Predictive algorithms for patient eligibility and risk assessment.
Personalized recruitment strategies based on demographics, genetics, and clinical history.
Improving patient diversity and trial inclusivity using AI tools.
Real-time recruitment monitoring and performance analytics.

AI techniques for handling large-scale clinical trial datasets.
Machine learning and deep learning for outcome prediction and pattern recognition.
Natural language processing (NLP) for unstructured data in research.
Real-time anomaly detection and monitoring of trial data quality.
Integrating AI insights into EDC systems and CTMS.

Emerging AI technologies in predictive modeling, genomics, and biomarker discovery.
Integration with wearable devices, remote monitoring, and real-world evidence platforms.
Advancements in personalized medicine and adaptive clinical trials.
Global perspectives on AI governance, ethics, and regulation in research.
Preparing research institutions for ongoing AI-driven innovation and digital transformation.

AI for Clinical Trials and Research Training Course in Paris

Available Dates