Generative AI has rapidly evolved from experimental flashes of innovation into a blazing force reshaping the technological landscape. As the flames of GenAI spread, the future tech stack will be defined by hybrid architectures that blend foundation models, agentic AI, knowledge graphs, and domain-specific autopilots. Together with advances in federated learning, memory systems and compute infrastructure, these approaches will amplify human creativity, accelerate decision-making, and unlock collaborative innovation across the life sciences.

In today’s evolving healthcare landscape, Generative AI and Real-World Data (RWD) are transforming pharmaceutical R&D. This session introduces Merck’s RWDEx platform, showcasing AI-powered agents that enable real-time evidence synthesis, automate regulatory tracking, and deliver personalized analytics. Through real-world case studies, attendees will learn how these innovations accelerate study execution, bridge data silos, and improve patient outcomes—advancing the future of data-driven drug development.

Generative AI is transforming clinical trial operations by automating protocol authoring, regulatory submissions, and patient communications. This session highlights how generative AI streamlines workflows, enhances audit readiness, and supports compliance with FDA guidance. Attendees will learn practical strategies for responsible AI adoption, see real-world examples of automation, and understand the importance of human oversight in deploying generative AI for safer, more efficient clinical research.

In a rapidly evolving technology landscape for drug discovery, AI agents or agentic systems present new strategic opportunities and challenges. This talk highlights several key strategic considerations—platform agility, proprietary data, and physical-world validation loops. I share some perspective on a balanced approach for efficiency and innovation, and why integration first thinking is essential for navigating emerging opportunities in this dynamic landscape.

Generative AI is rapidly transforming pharmaceutical landscape, and prompting is becoming more than just a way to interact with models. It is emerging as a driving force behind innovation, accuracy, speed, and computational costs. As language models demand significant data center resources, even small changes in how we prompt can influence performance, sustainability goals, and associated expenses. This session explores how evolving prompt strategies and architectures are reshaping everything from drug discovery to clinical documentation and scientific decision-making.

Speakers will discuss how self-correcting prompt systems can improve reliability by refining outputs in real time. The conversation will also explore the surprising value of hallucinations, showing how unexpected or creative outputs can spark new scientific ideas while highlighting strategies to manage risks responsibly in high-stakes environments. The session will also spotlight the rising role of voice-based prompting, from hands-free lab workflows to natural, spoken interactions with intelligent agents. This evolutionary shift makes generative AI more accessible and impactful across all roles in pharma, including research, regulatory, and commercial teams. Whether you work at the bench, manage data, or shape strategy, this session offers practical insights into how modern prompting techniques can elevate your everyday work and unlock deeper value from AI.

The Innovation Showcase offers emerging companies (pre-commercial; founded in 2020 or later; focused on life sciences, pharma, clinical research, healthcare, or informatics/tech; and currently raising a late seed or Series A) an alternative to a traditional sponsored talk. This curated, in-track session features lightning talks and demos from startups developing tools for discovery, development, and clinical research, highlighting novel approaches, architectures, and deployment models. To discuss participation, contact Cindy Crowninshield at ccrowninshield@healthtech.com.

While AI dominates early-stage drug discovery discussions, pharmaceutical organizations struggle to connect these innovations to downstream clinical operations and real-world patient care. This panel tackles three critical gaps in current AI implementation: 1) integrating AI across R&D functions from target identification through clinical development, 2) leveraging digital health platforms and AI as the bridge between research insights and patient outcomes, and 3) enabling cross-team collaboration and external partnerships through shared AI infrastructure. Industry leaders from biopharma, hospitals, and digital health will debate what it really takes to break down silos between discovery, development, clinical, and commercial teams. Through candid discussion of real implementations, panelists will explore how to build communication channels that connect internal teams with external partners (hospitals, payers, and patients), creating an AI ecosystem that delivers measurable impact across the full therapeutic lifecycle.