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The Impact of Generative AI and the Future of Work

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Across industries, generative AI is augmenting processes and has proven that it is here to stay – especially in life sciences. With that, it’s predicted that generative AI could add as much as $4.4 trillion annually to the global economy. In this post, we’ll discuss generative AI and the future of work and why it’s important to embrace its benefits.

Guide to Understanding AI for Medical Writers

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The adoption of AI in medical writing has been growing in popularity. In fact, by 2030, the global AI in medical writing market is predicted to reach a value of $1.76 billion, growing at a CAGR of 12.44% from 2023 to 2030. This growth can be largely attributed to the many benefits of implementing AI. This blog will serve as a guide to understanding AI for medical writers. 

AI: The Future of Medical Writing

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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.

How Does Generative AI Work?

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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? 

Latest Innovations in Natural Language Generation in AI

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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. 

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