While artificial intelligence can accelerate scientific discovery, moving discoveries toward practical applications still听depends on听human collaboration. That idea shaped 91唐伯虎鈥檚 2026 Life Science Showcase: AI in Translational 91唐伯虎.听
Organized by the听听team within the听, the showcase has grown from about 60 attendees at its first event four years ago to听more than听280听registrants听this year.听Attendees from 91唐伯虎,听91唐伯虎 University Medical Center, industry, government, venture听capital听and nonprofit organizations gathered to explore how AI can help move research toward real-world use.听
The showcase is designed to 鈥減ull back the curtain鈥� on life sciences research听across 91唐伯虎听and听VUMC and听introduce that work to听partners听who听can help move discoveries toward application, said Chris Rowe,听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,听investors听and communities.
Margulies emphasized AI鈥檚 potential to help researchers integrate large datasets,听identify听new听insights听and accelerate discoveries听well听beyond what individual investigators or teams听can听accomplish听alone.听听
鈥淲e need to convert that computational breakthrough to real-world therapies and impacts,鈥� she said. She pointed to 91唐伯虎鈥檚 investments in computational infrastructure, interdisciplinary听research听and听cross-sector partnerships as essential to advancing translational research in the age of AI.听
Presentations from 91唐伯虎 and VUMC researchers,听along with听leaders from the pharmaceutical and technology industries, gave听attendees a view of how AI is being used across academic research, drug听discovery听and commercial development.听
A cross-disciplinary panel 鈥淲ill AI Replace Experimental Science?鈥� examined both the potential and听the听limitations of using AI in research.听Panelists described how AI can accelerate data analysis, molecular design, target听identification听and hypothesis generation while also sharing examples of computational findings that initially appeared meaningful but were听ultimately traced听to misleading clinical records, experimental听artifacts听or poorly structured data.听听
AI systems听remain听dependent on the quality and context of the information they receive,听according to the panel,听underscoring the need for researchers to examine and experimentally听validate听AI-generated predictions.听
Bennett Landman, director of the听,听urged attendees听in his closing comments听to look beyond individual technologies and molecular discoveries and consider how those advances听ultimately come听together to improve lives.听
鈥淚t is clear how AI can accelerate research and translate discoveries into meaningful outcomes,鈥� Rowe said. 鈥淭he power of events like the Life Science Showcase and the role of universities broadly are to convene people, enable partnerships and drive sustainable collaborations.鈥�
