A Responsible AI Regulatory Framework for Drug Development

Event Time

Originally Aired - Wednesday, March 13 12:00 PM - 1:00 PM Eastern Time (US & Canada)

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Event Location

Location: W110A


Event Information

Type: Other Educational Events

Session ID: EHL1

Title: A Responsible AI Regulatory Framework for Drug Development

Description: This poster will describe the possible applications of Artificial Intelligence (AI) to pharmaceutical R&D, new drug development, and the clinical trial and regulatory approval process. It will also highlight the specific regulatory roadblocks and public policy solutions necessary to establish a “Responsible AI” framework for these processes. This framework will include a discussion of risk management, bias, transparency, explainability, accountability, cybersecurity, and privacy. The poster will focus on three key themes: the innovation potential of AI in drug development, the current regulatory landscape and roadblocks, and public or company policy solutions to navigating this space responsibly.

Level: Introductory

Learning Objective #1: Identify applications of AI in drug development and define the elements of a “Responsible AI” framework

Learning Objective #2: Discuss the implications of using AI in drug development for the healthcare ecosystem, as well as the current regulatory landscape governing these uses

Learning Objective #3: Analyze and categorize the elements of a “Responsible AI” policy framework

Learning Objective #4: Evaluate how to adjust public policy and company policy frameworks and governance to address the emerging challenges of using “Responsible AI” in drug development

Learning Objective #5: Design a policy and advocacy strategy that enables AI to transform drug development within a “Responsible AI” framework


Session is a part of

Wednesday, March 13, 2024 - 12:00 PM
Emerging Healthcare Leaders Poster Sessions: Meet the Authors


Speakers


Continuing Education Credits

  • ACPE – 1 Credit(s)
  • CAHIMS – 1 Credit(s)
  • CME – 1 Credit(s)
  • CNE – 1 Credit(s)
  • CPD UK – 1 Credit(s)
  • CPHIMS – 1 Credit(s)

  • Tracks


    Categories

    Data & Information

    • Artificial Intelligence/Machine Learning

    Audience

    • Early Careerist
    • IT Professional
    • Student