Navigating through the AI hype
Observations from the first year in the fields of Biotech and Pharma
We approach the one-year anniversary of the launch of ChatGPT, and in this short span of time, AI has created ripples of innovative opportunities to generate new insights and improve processes in data management across the medicinal product lifecycle.
However, we are still at the early stages of the implementation.
In this blog post, we will look into some of the observations made from the first year of the AI hype and the effect it has had on biotech and pharmaceuticals.
The rise of in-house Large Language Models
Since the emergence of Large Language Models (LLM) such as ChatGPT, major pharmaceuticals and biotech corporations have begun developing their own in-house versions to safeguard their data from potential external exposure.
These major corporations understand that AI has the potential to revolutionize the way they handle their data.
From drug discovery to clinical trials and regulatory compliance, AI tools can optimise processes, extract valuable insights and drive more innovative workflows.
In fact, THE FDA has observed a growing number of drug and biological applications incorporating AI/LM components in the past year. These submissions cover various stages of drug development, spanning from drug discovery and clinical research to postmarket safety monitoring and advanced pharmaceutical manufacturing.
By developing their in-house LLMs, corporations as such can maintain complete control over their data. They no longer need to rely on external AI providers, reducing the risk of data breaches or exposure to competitors.
Accessibility to AI tools presents challenges
In contrast to their larger counterparts, smaller companies are faced with challenges in gaining access to AI tools – especially in relation to developing and training targeted models.
In particular, when it comes to utilising LLMs, significant financial investments are needed, and there is also a significant time commitment required to gain specialised knowledge. This time commitment is a luxury that smaller corporations may not be able to afford.
Thereby, the resource-intensive nature of AI implementation causes a digital divide where larger corporations have the means to embark on these endeavours while smaller players struggle to secure the necessary resources.
Bridging this gap will be a critical challenge in ensuring that the transformative potential of AI is accessible to a broader spectrum of businesses, fostering innovation and competition in our industry.
AI still needs to be tamed
However, it is crucial to acknowledge that AI implementation is still in its early stages.
Companies are in the process of figuring out how to effectively harness the benefits of AI, starting with tasks that can be automated and streamlined through AI technology.
Here, there has been a distinct focus on data summarisation. This is not surprising, considering the speed of which data volumes are growing in the Biotech and Pharma fields, and AI might just be the tool that efficiently scavenges large data volumes.
However, although effective, it is still necessary to keep an eye on AI. Because without ensuring that AI is fed clear and accurate data, there is a significant risk of ending up with incorrect or misleading results.
Human insight is not irreplaceable
Despite huge and fast improvements, AI is still not ready to be left to its own devices, and human oversight is still necessary to uphold data quality.
No matter how advanced the AI systems become, they can not replace the judgement and decision-making skills of real people.
In particular, a keen eye is vital in tasks like data validation, clinical trials and in other cases where the accuracy and reliability of results are crucial for success.
Because AI is no different than other tools, it has its strengths and weaknesses, and while it can greatly optimise efficiency in some cases, it is not effective for every task.
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