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 revolutionizing areas like drug discovery, bioinformatics, and personalized treatments is 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