Yseop automates and translates data into actionable language, enabling the simplification of once complex workflows.
Yseop’s AI-based technologies generate core elements of specialist medical reports, including clinical study reports (CSR), patient narratives, and more.
A recent study in JAMA Internal Medicine compared physician versus AI chatbot responses to patient’s questions asked on a public social media forum. Tim Martin, Yseop’s Vice president of Product and Development was featured in USA Today discussing this topic further.
In this blog post, we’ve highlighted the latest generative AI trends, updates and news articles from the past month.
The concept of generative AI and Large Language Models (LLMs) continues to grab headlines. Whether it’s the new Bedrock service or StableLM, LLMs are everywhere. For context, LLMs use deep learning techniques and data sets to understand, generate and predict content.
The latest AI solutions can expedite the drug discovery process and ensure accuracy in life sciences. Recently, Yseop’s CEO, Emmanuel Walckenaer was featured in FierceElectronics discussing generative AI trends and benefits in the drug discovery process.
Implementing Natural Language Generation solutions in life sciences can significantly help pass strict regulatory requirements – saving time and costs often caused by human error.
Generative AI in pharma is particularly gaining momentum, with many leading companies in life sciences already utilizing AI solutions and innovative technologies to increase productivity and efficiency across the board.
Using Large Language Models (LLMs) to create code, text or images, it’s no wonder that the concept of generative AI is booming.
Generative AI has become one of the hottest topics of 2023 to date – dominating headlines and sparking debate. While it has been around for years now, the latest and impressive capabilities are taking the world of AI by storm.
Natural Language Processing (NLP), Natural Language Generation (NLG), and Natural Language Understanding (NLU) are all critical pieces of AI, but the differences are commonly confused. At a high-level, NLU and NLG are components of NLP.
AI in financial reporting puts reliability, efficiency, and scalability at the forefront.
