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Unleashing the potential of large language models across pharma

The transformative power of ChatGPT and other large Language Models (LLMs) has ignited curiosity across various industries.

While their applications continue to diversify, the potential within life sciences, particularly the pharmaceutical sector, is yet to be fully explored.

Without a doubt, the unique challenges of the life sciences field present an opportunity for LLMs to revolutionise processes and drive innovation.

But with great power comes great responsibility. LLMs, though promising, pose inherent risks without the right auditing, compliance and guard rails.

Their strength hinges on the analytic platform or engine supporting them, emphasising the need for a robust infrastructure to maximise their functionality.

Despite these risks Deloitte recently revealed compelling statistics – 75% of leading healthcare companies are actively experimenting or scaling Generative AI across their enterprises.

Meanwhile, 82% are implementing governance structures, and a staggering 92% see the potential for Generative AI to enhance efficiencies, while 65% recognise its role in enabling faster decision-making.

LLMs play a crucial role in designing preclinical assays by analysing scientific literature. They streamline the identification of suitable methods, cell lines and animal models, easing what can be a fairly onerous process.

In clinical trials, LLMs have the potential to significantly improve protocol design and engagement. By providing insights from past protocols, they reduce amendments, ensure cleaner designs and can even serve as real-time chatbots for study teams, enhancing compliance and efficiency.

Life sciences organisations generate vast amounts of heterogeneous data, making knowledge graphs essential for integration. LLMs focused on proprietary knowledge graphs unify data, enabling data-driven decisions.

They support informed decision-making in research, discovery and development. Additionally, LLMs aid in precision marketing by interpreting unstructured data to target patient populations effectively.

In the highly sensitive and regulated sectors of pharma and healthcare, however, utilising LLMs demands compliance with regulations such general data protection regulation (GDPR). Security breaches and concerns loom large, necessitating a vigilant approach to risk, fraud and cybersecurity.

Crafting LLMs without the guidance of analytic expertise can result in scrambled or damaging outcomes, underscoring the importance of skillful implementation.

Traceability – in terms of data used to train the models – becomes paramount. LLMs need reliable resource call-backs, and some human oversight, to validate outcomes in pharma.  Ensuring traceability not only enhances the credibility of results but also provides a safety net in the dynamic landscape of pharmaceutical research and development.

To harness the full potential of LLMs, an integrated approach is key.

LLMs are not stand-alone entities – they are only as robust as the analytics and computational infrastructure supporting them.

Secure environments, hybrid statistical compute and advanced analytics form the backbone, ensuring a holistic and resilient ecosystem for LLMs to thrive.

Despite challenges and ethical considerations, the transformative impact on research, discovery, development and commercialisation is evident.

By adopting a thoughtful approach that includes robust analytics, secure environments and a keen focus on risk management, organisations can harness the power of LLMs. This integrated strategy unlocks new levels of precision, efficiency and quality.

The future is bright, but it requires a strategic and vigilant hand to guide its potential toward groundbreaking advancements.

Jim Box is Principal Data Scientist, Healthcare and Life Sciences Solutions at SAS. Go to sas.com