Abstract
The advances in cancer nanotechnologies and current efforts focused on data sharing, along with the associated challenges, have been summarized in the first part of this review. Herein, we explore the potential of Large Language Models (LLMs) to enhance user experience, using the federally funded data repository caNanoLab as a case study. By training the LLM on caNanoLab data, we demonstrate its ability to provide more comprehensive search results, well-guided data entry, and a personalized, assisted search experience. We also discuss how integrating LLMs would optimize user experience, making it easier for researchers to navigate complex data and find relevant information. Our findings suggest that LLMs have significant potential to transform cancer nanotechnology data analysis and user experience, potentially paving the way for more efficient and effective advancements in cancer research. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Emerging Technologies.