Life Sciences AI Summit

This session will focus on how the FDA and EMA are shaping AI in life sciences, providing a roadmap for companies to meet evolving regulatory standards. Topics of discuss will include:

• Navigating FDA guidance on AI medical devices, including Predetermined Change Control Plans (PCCPs) and clinical decision support software.
• Exploring EMA alignment with the EU AI Act and its regulations for AI in the life sciences
• Examining case studies showing how companies navigate FDA and EMA directives
• Comparing key differences and similarities between FDA and EMA AI regulations

This session focuses on the potential litigation challenges arising throughout the lifecycle of AI-driven products in the life sciences sector. By examining legal risks from R&D to post-market, attendees will gain actionable insights into litigation strategies and how to mitigate risks in real-time. Topics of discussion will include:

• Clarifying product liability in cases of AI errors during commercialization, and how fault is determined under current legal frameworks
• Managing data privacy litigation risks, particularly concerning patient data misuse during AI-powered clinical trials
• Navigating post-commercialization legal risks, including real-time performance monitoring, algorithm failures, and regulatory non-compliance
• Exploring recent case law surrounding AI-generated content and proprietary algorithms, including practical litigation strategies for life sciences companies

This session explores how regulatory sandboxes create a safe space to test AI applications in life sciences while ensuring legal compliance. Session leaders will detail how sandboxes foster innovation, manage risks, and align with global regulations. Topics of discussion will include:

• Using regulatory sandboxes to test AI in a compliant setting
• Navigating global regulations for AI in life sciences
• Managing AI bias and ethical issues during testing
• Strengthening partnerships and cross-border compliance
• Addressing legal challenges including antitrust risks in AI-driven innovations

This session considers the unique capabilities of generative AI in life sciences, particularly in drug discovery, personalized medicine, and synthetic biology. Topics of discussion will include:

• Understanding how generative AI is being used to create novel compounds, optimize molecular structures, and reduce the time needed for R&D
• Using generative AI to develop individualized treatment plans and predict patient responses to therapies
• Addressing the legal and regulatory hurdles specific to generative AI in these areas, and how companies are managing these complexities in line with FDA and EMA guidelines
• Examining the ethical concerns around using AI to create synthetic biology, potential risks of generating novel compounds, and ensuring patient safety

As AI becomes integral to innovation in the life sciences, it creates complexities in IP law. Understanding how to protect AI-driven discoveries while navigating uncertainties of inventorship and ownership is mission critical for staying ahead in this rapidly evolving landscape. Topics of discussion will include:

• Evaluating patentability and the boundaries of IP protection for AI-generated innovations
• Determining ownership rights when AI contributes to the inventive process
• Addressing joint ownership scenarios and the impact on licensing agreements
• Mitigating infringement risks when AI uses existing IP for training
• Distinguishing between proprietary data used for AI training and AI-generated datasets, assessing copyright and data ownership risks

This session will explore cutting-edge applications of AI to optimize trial design, improve data management, and navigate the regulatory landscape. Topics of discussion will include:

• Using AI to enhance trial design, boost recruitment with synthetic data, and address biases for more diverse and fair trial population
• Implementing FDA guidance on Clinical Decision Support Software while ensuring data management consistency and reducing errors
• Exploring the legal and ethical challenges of AI in patient engagement, from recruitment through participation
• Leveraging AI-driven post-trial analysis for more efficient regulatory submissions and accuracy in reporting
• Understanding the FDA and EMA’s evolving regulations on AI tools, including new rules for AI in clinical trials

This session will explore how to create and manage AI collaborations that not only advance research but also translate into real-world clinical outcomes. Topics of discussion will include:

Building seamless AI partnerships that bridge research and clinical use, driving real-world innovation in diagnostics, drug discovery, and personalized care. Topics of discussion will include:

• Mastering collaboration challenges such as data sharing, model deployment, and regulatory compliance
• Avoiding common pitfalls like misaligned goals, data privacy issues, and technical barriers
• Crafting agreements that prioritize patient safety, data security, and ethical AI practices
• Ensuring post-partnership success by integrating AI solutions into clinical workflows
• Addressing technical challenges like system compatibility

This panel will guide attendees through the evolving landscape of privacy laws, including GDPR and HIPAA, and offer practical solutions to balance the innovation of AI-driven technologies with the critical need to protect sensitive patient data. Topics of discussion will include:

• Balancing risks and rewards of using large datasets in AI-driven life sciences
• Ensuring data security and privacy
• Navigating GDPR and HIPAA compliance for healthcare organizations leveraging AI, addressing breach notifications, data rights, and the role of Data Protection Officers
• Aligning HIPAA’s Privacy and Security Rules with evolving AI technologies and ensuring data protection through anonymization and encryption techniques
• Mitigating bias and ensuring ethical AI use in data-driven applications, harmonizing AI ethics with patient consent and safeguarding sensitive data
• Safeguarding patient data while staying compliant with global data privacy laws
• Strategizing for cross-border data transfers

AI’s role in the marketing and promotion of drugs and devices cannot be understated. This session will outline the multifaceted applications of AI in promoting life sciences products, while also highlighting the legal intricacies and considerations vital for ensuring compliance and mitigating potential risks. Topics of discussion will include:

• ASSESSING THE ROLE OF AI IN DRUG AND MEDICAL DEVICE MARKETING STRATEGIES
• Unpacking FDA’s Draft Guidance on marketing submission recommendations for a Predetermined Change Control Plan (PCCP) for Artificial intelligence/Machine Learning-Enabled Device Software Functions (April 2023)
• Strategizing for minimizing potential legal risks in AI-empowered marketing campaigns

This session will explore how AI innovations are driving the development of software-based treatments while aligning with evolving regulatory frameworks – topics of discussion will include:

• Examining how AI-powered patient monitoring is transforming remote healthcare
• Understanding AI’s impact on wearables, predictive analytics, and personalized patient care
• Addressing concerns about AI in patient data management and navigating regulatory requirements
• Decoding FDA’s evolving stance on AI-integrated software as a medical device (SaMD)
• Considering the ethical and regulatory implications of AI’s advancing diagnostic capabilities

AI has transformed clinical decision-making, but it also introduces significant risks, including data privacy concerns, regulatory challenges, and potential bias in treatment decisions. This session explores both the benefits and dangers of AI-powered healthcare. Topics of discussion will include:

• Improving clinical decision-making with AI, enhancing speed, accuracy, and data-driven treatment options
• Navigating FDA guidelines and implementing post-market surveillance for continuous AI system updates
• Transforming medical imaging and precision medicine, enabling personalized treatments and reducing errors
• Addressing data privacy concerns while ensuring robust datasets for effective AI model training
• Overcoming barriers to AI integration in healthcare systems while ensuring regulatory compliance and patient outcomes

As AI becomes more embedded in life sciences, ensuring fairness, equity, and accountability is essential to prevent bias and promote ethical applications. Topics of discussion will include:

• Tackling AI bias in drug development, clinical trials, and diagnostics
• Addressing bias sources from data collection to algorithm design to ensure fair outcomes
• Evaluating the impact of the EU AI Act on fairness, transparency, and accountability
• Exploring tools to diversify data and enhance model transparency for fairer AI decisions
• Balancing ethical considerations and real-world challenges where AI decisions confront traditional norms