- Understanding the Pharmacy Card Model
- The Program Lifecycle: From Design to Delivery
- A Day in the Life: Sponsor & Sites
- Proof of Performance: Feedback and Case Studies
Archives: Agenda
AI-enabled Drug Discovery and Development: From Bench to Bedside
- • ‘Lab-in-the-loop’ approaches: integrating AI models and experimental platforms for target discovery and precision medicine
• AI enabled precision oncology: biomarkers, patient stratification and clinical trial design
Chairperson’s closing remarks
Chairperson’s closing remarks
Afternoon refreshments and networking
Enhancing the patient journey in oncology trials: Patient insights and practical innovation
- Considering the full patient burden landscape when planning oncology clinical trials to alleviate patient burden effectively
- Recognizing disparities in burden and understanding differences across patient populations
- Incorporating patient input when designing protocols to ensure that needs are met
From Tension to Trust: Aligning Medical monitoring and Clinical Operations in Complex Oncology Trials
- Cultural and behavioral dynamics
- Differing incentives and pressures
- Communication failures during safety events
- Building trust under time pressure
Analyzing patient adherence to drug therapy effectively: A practical guide to methods and metrics for increased efficiency in oncology drug trials
- Definition, Importance and Measurement of adherence
- Key adherence methods and matrix
- Increasing efficiency of adherence through enhancement and implementation
Lunch and networking
WORKSHOP: Responsible AI in oncology clinical trials: Applying the AI rights initiative to recruitment, patient data, and patient trust
Concept: This interactive workshop introduces the AI Rights Initiative as a practical framework that sponsors, CROs, and recruitment vendors can use to evaluate AI systems used in oncology trials. Through a short panel discussion and facilitated table conversations, participants will explore how recruitment algorithms, digital tracking technologies, and patient data analytics intersect with patient rights and regulatory responsibilities
Takeaway: Attendees will leave with practical governance questions that can help organizations implement AI responsibly while maintaining patient trust in clinical research