Without a doubt, advanced AI technologies are reimagining the future of medical writing. Historically, medical writers have been faced with inefficient, monotonous processes and have been seeking a solution to save time and ensure accuracy. Enter generative AI, which uses Large Language Models (LLMs) and advanced machine learning to create human-like text, pulling from existing data and experiences to generate new content.
Generative AI has quickly emerged as a leading trend – impacting the way humans perform tasks and work across industries. According to McKinsey & Company, generative AI’s impact on productivity has the potential to add trillions of dollars to the global economy. As generative AI technologies continue to evolve, it’s important to understand the best ways to implement these models. In this blog post, we’ll address a commonly asked question: how does generative AI work?
Natural language generation (NLG) is a critical piece of AI. Specifically, NLG is a subsection of Natural Language Processing (NLP). NLG software turns structured data into written reports, providing explanations and narratives at a rapid pace. We’ve seen many advancements stemming from NLG. In this blog post, we’ll discuss what’s new in natural language generation in AI.
This year, Yseop was a part of DIA Boston. Alongside Eli Lilly, the teams led a session called, “Reimagining Scientific Writing with Generative AI.” Overall, it was a successful session with over 140 attendees.
With Yseop Copilot, scientific writers can automate their workflows while staying GxP compliant, making it the ideal choice for non-clinical and clinical documents. Compared to traditional LLMs like ChatGPT, Yseop Copilot guarantees optimum security and compliance.