The Artos Blog

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,

Generative AI’s expansive capabilities present opportunities to tackle a variety of tasks that previously were not addressable by software. The natural temptation that comes from this is to see everything as a solution that AI might solve. And while that might be true at a surface level, when you break down exactly what the “jobs to be done” are for software in industries like the life sciences, that’s not so true.

It’s now easier than ever for life sciences firms - even those with relatively little AI expertise - to build AI tools. In fact, that’s what makes this particular wave of AI so much more powerful than those before it.

As life sciences organizations increasingly explore the potential of Generative AI in transforming their workflows, effectively evaluating different aspects of performance for various Generative AI applications becomes critical. Many aspects of these applications can be assessed, but one that is often only assessed in a highly manual fashion at present is model choice. As organizations work to identify what Generative AI models they want to leverage, they find themselves manually switching off models or not looking to optimize at the model level at all.