AI Takes Off in Life Sciences

While Artificial Intelligence is probably the hottest topic in general since the release of ChatGPT in 2022, it already has been applied in Life Sciences for quite some time and in some areas with great success. There are areas, such drug discovery where AI is widely adopted and even has proven and almost daily keeps proven effectiveness. In many other areas we observe a larger struggle to find useful applications of AI.   

Life Sciences companies still struggle to fully capitalize on the advantages of AI

As we have seen with past trends such as Big Data, Real World Evidence, Virtual Reality, there is a lot of potential, however Life Science companies struggle to find the right application. Let alone to take full advantage of these innovations. It can be stated that LS companies in general are significantly less successful in leveraging digital innovations in comparison to other industries.   

We see life science companies apply AI on a very ad-hoc basis, which is largely impulse driven. These impulses can be an employee or department wanting to use a specific tool or a large challenge where a fast solution is needed. At the same time, we don’t observe any fundamental assessments and roadmaps to determine the best use and investment of AI across companies. 

How Life Sciences can leverage AI for their business 

In our recent report on AI in Life Sciences we leveraged our experience in digital transformation within the Life Sciences industry to provide an overview of the current state, opportunities, and use cases of AI for Life Sciences companies. To access the impact and opportunities of AI, we focused on the pharmaceutical and biotech companies and their core functions. We used this split of functions from Discovery ​to Exploratory Development, Confirmatory Development, Launch-Global Access up to Commercialization.

In addition, we provide an overview of the development and current status of the industry, offer insights into the different categories of artificial intelligence, and conclude the report with practical case studies showing how life sciences companies are already generating business value. Here’s a summary of the contents of the AI in Life Sciences report:

AI in Life Sciences Report - Table of Contents

1. The Story of AI in Life Sciences: The long prelude of AI from the mid-20th century, evolving from machine learning to deep learning, culminating in the ChatGPT moment. 

2. Beyond GenAI: An overview of 12 categories of Artificial Intelligence and their use cases in the Life Sciences industry. 

3. AI in Drug Discovery: Narrow AI is now accelerating pipelines; as algorithms and applications advance, autonomous systems could take on discovery feats once solely imaginable by humans. 

4. AI in Exploratory Development: AI will become an invaluable tool to push the boundaries of what is possible in exploratory drug development. 

5. AI in Confirmatory Development: As predictive analytics and connectivity improve, AI integration will evolve from disjointed support tools to interconnected drivers of confirmatory trials through dynamic intervention, continuous assessment, and autonomous oversight. 

6. AI in Launch and Global Access: With rigorous calibration, AI-directed launch research could significantly improve global speed of access and impact at scale while ensuring affordability and sustainability. 

7. AI in Commercialization:  AI integration will move from disjointed support tools to interconnected drivers of access and launch, transforming fragmented analog processes into equitable digital-first patient access engines. 

8. Creating Business Value with AI: There are so many possibilities that companies may find it challenging to choose the right path. However, there are good real-world examples of how it can work.