For a long time, the idea of using intelligent automation in the life sciences sector, such as improving the reporting of adverse drug reactions, was hindered by the enormous amount of work required to set it up and make it fully functional.

However, this obstacle is now being overcome thanks to the development of large language models (LLMs) that power generative artificial intelligence. These models are enhancing compliance and could bring about a significant transformation in the pharmaceutical industry.

The industry may have a rare opportunity to revolutionize its operations with this AI technology, but it needs to be able to scale it up and address the unique challenges it faces.

New Possibilities

Machine learning and LLMs are making it possible to quickly search for new drugs, conduct more effective clinical trials, and speed up regulatory approvals. This, in turn, leads to highly targeted marketing materials.

Almost every aspect of the pharmaceutical industry is being transformed by generative AI, which is changing the way businesses operate and could unlock billions of dollars in value.

According to estimates, this technology could bring in between $60 billion and $110 billion annually for the pharmaceutical and medical product industries.

This is mainly because it can increase productivity by accelerating the discovery of potential new drugs, expediting their development and approval, and enhancing their marketing.

Efficient Data Transformation

When dealing with large amounts of data from various sources, such as in safety monitoring, it takes a lot of administrative work to compile and make sense of the data.

Recent developments in machine learning and AI offer significant potential for process transformation, not only in terms of efficiency but also in terms of accuracy, provided the software knows what it's looking for.

Improving Data Compilation

The training burden has been a major issue with machine learning solutions so far. However, with LLMs, a single technological solution can handle all variants of incoming data, eliminating the need to teach AI models what to look for.

Applying LLM-RAG technology to change adverse event case input has resulted in efficiency gains of up to 65% and 90%+ data extraction quality and accuracy in early trials. This has the potential to significantly simplify some of the pharmaceutical industry's most data-intensive operations.

Embracing Change

Concerns about compliance or reliability that previously held back the adoption of smarter automation are giving way to a strong desire to adopt new versions of the technology that directly address these issues and provide measurable improvements in productivity and efficiency.

"To build momentum for change, organizations should create teams of early-adopter champions to shape the deployment of generational AI use cases and prove their value."