The Artos Blog

Generative AI has shown immense potential in various industries, and the life sciences sector is no exception. From automating document creation to generating regulatory submissions, AI-driven systems offer an exciting frontier for increasing efficiency and accuracy. However, there is a notably low margin for error in the space. In this high-stakes arena, the bar for validating generative AI systems is significantly elevated.

Artificial Intelligence (AI) is revolutionizing industries worldwide, and the life sciences sector is no exception. With the increasing complexity of regulatory requirements, life sciences companies are turning to AI solutions to streamline the creation of regulatory documentation. From accelerating drug approvals to ensuring compliance with global standards, AI promises significant advancements. However, one big hurdle that we’ve seen that many have underestimated is the cost of change management. Many of the processes AI stands to change have been solidified over decades, making them habits that require intentional effort to change.

The team here at Artos figured that it might be timely to write about conferences. With folks coming back from summer vacation, conferences are picking up again. AI has been and will continue to be a central theme at many of these as the biotech, pharma, and medical devices spaces continue to grapple with how to best deploy these solutions to create value for the sector. And conferences are an industry staple for the life sciences - a convergence the life sciences space in particular relies on to learn and progress.

Enterprise AI applications are powerful tools that combine the capabilities of AI models, such as large language models (LLMs), with enterprise-level software systems. These applications solve specific business problems by creating workflows that automate tasks, generate insights, or improve decision-making. Given the particularly sensitive nature of the data many biotech, pharmaceutical, and medical device companies are considering providing to enterprise AI applications, it is particularly critical for these organizations to thoroughly understand the security risks of this type of tool, especially in light of some of the negative press around security that AI systems like ChatGPT received in the early days following its release.

In the fast-paced and highly regulated world of life sciences, efficiency and precision are paramount. Whether it's developing new pharmaceuticals, conducting clinical trials, or preparing regulatory submissions, the...

The value-add of AI is limited by how trustworthy the AI systems being used actually are. The more trusthworthy AI systems are, the more companies are able to be hands-off with the work done by AI. On the other hand,