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AI in Preclinical and Clinical Development Fundamentals
"AI in Preclinical and Clinical Development Fundamentals" teaches AI's role in patient stratification, outcome prediction, and trial optimization. Participants learn to apply ML models while ensuring patient data confidentiality. Cybersecurity measures covered include access controls, anonymization, and model auditability—crucial for maintaining trust and regulatory compliance in clinical AI systems.
Provider Information
Tonex, Inc.
6275 W. Plano Parkway, Suite 500
Plano, TX 75093
Course Overview
None
Learning Objectives
- Understand the applications of AI in preclinical and clinical drug development.
- Analyze how AI can optimize clinical trial design, patient recruitment, and data analysis.
- Evaluate the impact of AI on improving the efficiency and success rates of drug development.
- Discuss the regulatory aspects and data privacy concerns associated with AI in clinical trials, emphasizing the need for strong cybersecurity measures to protect patient data.
- Learn about AI-powered solutions for pharmacovigilance and real-world evidence generation.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.