Day ThreeThursday, 26 March 2026 - Western European Summer Time (WEST)

Pharmaceutical companies are increasingly evaluating whether to develop technologies in-house or partner with external innovators, this is no different when it comes to digital technologies. In-house development can provide greater control and business alignment while external innovators often bring fresh ideas and alternative viewpoints. This panel of partnership leaders explore the balance between in-house development and the continued value of external partnerships.

• The current balance between in-house and external development in Pharma
• Benefits of external partnerships in pharma innovation
• Case studies and real-life examples

Investment criteria for pharmatech startups vary significantly across funding stages. This panel provides insights into what corporate venture capitalists prioritize when evaluating Seed to Series B pharmatech companies. How CVCs must align with strategy of their parent company, how funding approaches differ based on company size and focus, and strategies for building investor confidence in AI healthcare solutions.

• What CVCs look for in pharmatech startups
• How funding approaches change according to parent company priorities
• Building investor confidence in AI-driven healthcare solutions

As general trust in AI grows, a paradox exists in how patients trust general AI versus pharmaceutical-developed AI solutions. This panel addresses this trust gap and explores communication strategies to effectively highlight the benefits of healthcare-specific AI applications. Ensuring consistent quality of AI-driven advice across diverse patient populations remains a critical challenge.

• Addressing levels of trust in general AI vs. pharma-developed AI
• Communication strategies to highlight healthcare-specific AI benefits
• The role of data security in patient trust

Integrating AI into clinical workflows presents both significant opportunities and challenges. Our group of experts explore these pros and cons and share practical approaches to AI implementation in clinical settings, strategies for addressing workforce concerns about automation, and showcases successful case studies of AI adoption in clinical operations.

• Integrating AI into existing clinical workflows
• Addressing job displacement fears and workforce transformation
• Case studies of successful AI implementation in clinical settings

Ensuring the safety of healthcare innovations are crucial and have long been regulated by various bodies. Regulatory frameworks significantly impact the pace and direction of digital health adoption. Here we examine how regulations influence digital innovation in drug discovery, development, and clinical applications. Understanding the regulatory landscape is essential for successful digital health implementation. Key Points:

• Impact of regulations on drug discovery and development
• Regulatory considerations for AI/ML/Digital Twins in literature analysis
• Navigating regulatory pathways for digital health innovations

AI is accelerating the advancement of precision medicine approaches. This panel explores the future of healthcare through the lens of personalized treatments, examining how AI is breaking down traditional barriers to precision medicine implementation, and the implications for patient care. As computational capabilities evolve, we're witnessing unprecedented opportunities to tailor medical interventions to individual genetic profiles, lifestyle factors, and environmental conditions.

• How AI-driven personalization is reshaping treatment paradigms
• What barriers are being broken by AI
• Implementation challenges and solutions for precision medicine adoption across healthcare systems
• Balancing technological innovation with ethical considerations in personalized healthcare delivery

Large Language Models (LLMs) have diverse applications within pharmaceutical companies. Our panel explores these usages, including applications like virtual chemists, and examines the complex relationship between patient-facing LLMs and public trust in these technologies, as well as internal uses.

• How LLMs are being used by Pharma
• Applications in virtual chemistry and drug discovery
• Patient-facing LLMs and considerations for building general trust