Workshop A – AI and ML Tech Boot Camp: A Guide to Understanding AI and Mel Technologies for Life Sciences Counsel and Executives

Feb 20, 2024 9:00am – 12:30 PM

Zheng Yang
Worldwide Head, AI/ML Strategy and Solutions
Healthcare and Life Sciences

Amazon Web Services

Anna Gressel
Paul Weiss

Alejandra Parra-Orlandoni
VP, Ethical Innovation & Privacy, Global Portfolio Division

This interactive workshop will provide legal, regulatory and compliance professionals in the life sciences with a solid understanding of the technology driving AI, ML and deep learning. Industry thought leaders will provide an in-depth analysis of key concepts, challenges, regulations, and practical insights, enabling participants to adeptly navigate the confluence of these technologies and the life sciences.

Topics of discussion will include:

  • Generative AI v. Traditional AI
    • Defining basic distinctions, characteristics, and foundational tech that separate generative AI from traditional AI
    • Understanding how generative AI is revolutionizing areas like drug discovery, bioinformatics, and personalized treatments relative to traditional AI in the life sciences
  • Fundamentals of AI and ML
    • Defining what Artificial Intelligence (AI) and Machine Learning (ML) are
    • Differentiating between AI and traditional programming
    • Understanding supervised, unsupervised, and reinforcement learning
    • Exploring Natural Language process, neural networks, and deep learning
  • AI Applications in Life Sciences
    • Exploring real-world applications of AI and ML in the life sciences
    • Understanding AI capabilities for drug discovery, clinical trials optimization, disease diagnosis, medical imaging, personalized medicine, and more
  • The AI Lifecycle: Data, Algorithms, and Model Training
    • Developing data collection and preprocessing:
      • Quality, bias, privacy, and security considerations
    • Selecting appropriate algorithms and models for specific tasks
    • Harmonizing the training process
      • Supervised learning, validation, and testing
  • Challenges and Risks in AI and ML
    • Anticipating bias and fairness issues in AI algorithms and their impact on decisions
    • Integrating ethical considerations when using AI in medical decision-making
    • Meeting regulatory and legal challenges
      • FDA regulations, GDPR compliance, intellectual property, and liability
  • Business and Compliance Considerations
    • Identifying business opportunities and ROI through AI adoption to be more effective
    • Ensuring compliance with relevant regulations, including FDA guidance and GDPR
    • Managing risks associated with AI implementation and usage
    • Fine-tuning transparency – what does it mean, and to whom