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This story highlights how the Marriott Library used AI to improve student employee staffing in interlibrary loan services. By analyzing historical request data and generating predictive heatmaps, Acquisitions Supervisor Annelise Nicholes Xiao aligned student schedules with actual demand—reducing idle time, improving service quality, and creating a more stable work environment. The project shows how predictive AI can streamline operations and elevate the student and patron experience, with further applications already underway.

At the University of Utah’s Utah NeuroRobotics Lab, artificial intelligence is driving breakthrough research in bionic technology. Led by Jacob A. George, the team develops AI-powered prosthetic and assistive devices that restore movement and sensation for individuals with neuromuscular impairments. Their work blends engineering, medicine, and machine learning to create intuitive, life-changing technologies—many of which have already produced compelling patient outcomes and inspired new startup ventures. Through deep interdisciplinary collaboration, the lab is shaping the future of bionics and redefining what’s possible in assistive healthcare.

At University Connected Learning, artificial intelligence is helping streamline communication between systems that traditionally don’t work well together. Program Coordinator Taber Tang used AI to automate the once time-intensive process of creating Outlook events for classes—reducing the workload from two to three full days to just three hours. This efficiency not only saves significant staff time but also minimizes errors and allows the team to focus on more meaningful work.

A multidisciplinary team at the University of Utah is using artificial intelligence and computer vision to develop advanced imaging biomarkers that enhance cancer diagnosis, prognosis, and treatment planning. Led by Beatrice Knudsen, one key research direction focuses on creating computational tissue biomarkers, while the broader team integrates histopathology, radiology, and clinical data into multi-modal algorithms. Supported by major federal grants and cross-department collaboration, this work is advancing precision cancer care and generating impactful publications and training opportunities.

University of Utah Department of Internal Medicine Research Associate Professor George G. Vega Yon and his software development team use agentic AI as a human-guided co-developer to accelerate CDC-funded public health projects. AI assists with coding, testing, and rapid prototyping while researchers remain fully in the loop, enabling faster responses to real-world needs such as measles outbreak modeling.