While artificial intelligence can accelerate scientific discovery, moving discoveries toward practical applications still depends on human collaboration. That idea shaped Vanderbilt’s 2026 Life Science Showcase: AI in Translational Research.
Organized by the Industry Collaborations team within the Center for Technology Transfer and Commercialization, the showcase has grown from about 60 attendees at its first event four years ago to more than 280 registrants this year. Attendees from Vanderbilt, Vanderbilt University Medical Center, industry, government, venture capital and nonprofit organizations gathered to explore how AI can help move research toward real-world use.
The showcase is designed to “pull back the curtain” on life sciences research across Vanderbilt and VUMC and introduce that work to partners who can help move discoveries toward application, said Chris Rowe, executive director for industry collaborations.
Susan Margulies, vice provost for research and innovation, formally opened the event by emphasizing that translational research relies on strong ties between researchers, clinicians, engineers, investors and communities.
Margulies emphasized AI’s potential to help researchers integrate large datasets, identify new insights and accelerate discoveries well beyond what individual investigators or teams can accomplish alone.
“We need to convert that computational breakthrough to real-world therapies and impacts,” she said. She pointed to Vanderbilt’s investments in computational infrastructure, interdisciplinary research and cross-sector partnerships as essential to advancing translational research in the age of AI.
Presentations from Vanderbilt and VUMC researchers, along with leaders from the pharmaceutical and technology industries, gave attendees a view of how AI is being used across academic research, drug discovery and commercial development.
A cross-disciplinary panel “Will AI Replace Experimental Science?” examined both the potential and the limitations of using AI in research. Panelists described how AI can accelerate data analysis, molecular design, target identification and hypothesis generation while also sharing examples of computational findings that initially appeared meaningful but were ultimately traced to misleading clinical records, experimental artifacts or poorly structured data.
AI systems remain dependent on the quality and context of the information they receive, according to the panel, underscoring the need for researchers to examine and experimentally validate AI-generated predictions.
Bennett Landman, director of the Vanderbilt Lab for Immersive AI Translation, urged attendees in his closing comments to look beyond individual technologies and molecular discoveries and consider how those advances ultimately come together to improve lives.
“It is clear how AI can accelerate research and translate discoveries into meaningful outcomes,” Rowe said. “The power of events like the Life Science Showcase and the role of universities broadly are to convene people, enable partnerships and drive sustainable collaborations.”
