The Impact of Generative AI on Regulated Industries

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Using Large Language Models (LLMs) to create code, text or images, it’s no wonder that the concept of generative AI is booming. To add to the frenzy, VC firms have invested over $1.7 billion in generative AI platforms over the last three years, with AI-enabled drug discovery and software coding receiving the bulk of the funding. 

While tools like ChatGPT are groundbreaking, its outputs are built on predictive engines and can be misleading. Such models lack the ability to understand the complexity of human language and make sense of each word’s meaning. With that being said, this makes it common for false information to be produced when using the ChatGPT interface. 

Integrating Generative AI

When using the open web as a source of information, all companies – especially those in regulated industries – should proceed with caution. So, what are the fundamental things those in life sciences and finance should take into account when evaluating generative AI and LLMs? 

  1. Data Security 

In regulated industries like life sciences, enterprise software for content generation needs to consider many fundamental customer needs: data isolation and security, transparency and explainability of AI models, bias and fairness and the auditability of results. With technical guardrails and specific domain training, deploying such tools can be helpful for first drafts of regulatory documents. 

In life sciences and drug introduction, medical writing teams are working with confidential and sensitive patient data. As they exist today, leveraging open platforms like ChatGPT is not a viable, or secure, option. 

  1. Change Management

It’s important to recognize that implementing this technology can be a huge change for highly-skilled workers. Change of this magnitude often brings skepticism, fear, and uncertainty. When implementing any sort of technology, it is critical to get buy-in and understanding from teams (like medical writers) to ensure they are on board. Look at it as augmentation and not replacement. Instead, consider it as a digital copilot. 

  1. Human-Centered AI 

The key to success in implementing a digital copilot or AI-powered platform is looking at it as the amplification of human abilities. Training models using LLM and symbolic AI is critical to ensure reliable, transparent, and explainable results. 

Yseop has used components of generative AI to build their NLG Yseop Copilot platform. As the industry continues to debate the role of LLMs in a regulated environment, Yseop believes there is an incredible opportunity to leverage this technology safely. Yseop’s platform brings Natural Language Generation (NLG) and other advanced technologies together to offer automation fit for regulated industries. 

Yseop’s Role

At Yseop, we believe that any AI system should be human centric and amplify human abilities. Our products are designed to be copilots to help human efficiency and make work more productive, enjoyable and fair. Yseop’s sole focus is using generative AI for content creation in regulated industries. Without a doubt, the automation of content in any regulated document is complex. Yseop is using a combination of generation technologies to deliver the greatest automation value for customers. Leveraging LLMs in our platform has already started and is proving to be a great technology asset.

As this technology is here to stay, staying ahead will be crucial for how firms across regulated industries excel. Working with platform providers who implement this technology with transparency and explainability is the key to success.

To learn more about how Yseop can help your organization, please contact

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