PANEL DISCUSSION: Is clinical trial exclusion criteria too stringent?

  • How is the industry approaching diversity and what should the primary focus be?
  • Increasing accessibility to trials whilst simultaneously maintaining an appropriate level of patient safety
  • Assessing the link between restrictive eligibility criteria and rates of serious adverse events to make informed decisions on expanding exclusion conditions
  • Alternative solutions to improve patient numbers amidst pressures to hit recruitment milestones
  • How have increased pressures of patient recruitment lead to the questioning of traditional enrollment methods?

Chair: Cathy Scharf, Patient Advocate, Patient Advocates of the East Bay

KEYNOTE: Automating manual processes to improve decision-making and patient care

  • Automating Adverse Event Evaluations: Enhancing speed and accuracy using AI.
  • Streamlining Clinical Protocol Training: Efficiently generating consistent training materials with AI.
  • Improving Medical Coding: Utilizing AI for accurate adverse event classification and regulatory compliance.
  • Reducing cycle times and operational costs and enhancing data accuracy to
    better clinical trial results

KEYNOTE: Designing trials to reduce patient and site burden

  • How to create a patient-centric clinical protocol to obtain the necessary data and validate your primary objective
  • How biotechs are using tools internally and managing different stakeholders to pressure test themselves to have less complicated protocols
  • Looking at ways to ensure patients don’t go through a burdensome process
  • Championing collaborative sites for a streamlined trial

Ethical requirements and rights for patient disclosure and consent when implementing AI tools

  • How do we look at data transparency issues when scaling AI?
  • Case study on AI rights and a look at recently released survey discussing where things need to change
  • Without GDPR, what can be done to protect patients regarding data sovereignty?
  • Delving into the unique and amplified challenges for AI risk assessment
  • How we can effectively leverage AI in our heavily regulated environment?