Chat GPT, Bard, and other Large Language Models (LLMs) have sparked transformative discussions in various industries, including life sciences. However, their integration into the realm of life sciences encounters critical hurdles. As the industry demands factual precision within its complex domains, the current generation of LLMs presents more challenges than solutions. To navigate these limitations, a new iteration of Generative AI is required: one grounded in reality, fortified with data security measures, and adept in comprehending the intricate subject matter that underpins pharmaceutical and bioscience advancements.

Shaping the Narrative in Life Sciences

Chat GPT has redefined discussion surrounding innovation across multiple sectors, including life sciences and pharma. However, the excitement surrounding Generative AI in other industries does not seamlessly translate here. Conferences dedicated to life sciences consistently echo a common sentiment: an initial spark of enthusiasm followed swiftly by skepticism regarding the practical applications of LLMs. Recent instances of LLMs fabricating information, leading to personal humiliation of lawyers, scientists, and authors, have rightfully induced caution in the scientific community.

The prevailing issue lies in the mismatch between existing Generative AI capabilities and the nuanced demands of biological sciences. While current market leaders have produced impressive products, they lack specialization in biology's complexity and scientific rigor necessary for groundbreaking discoveries. In this 5 part series we explore the primary challenges faced by LLM applications in life sciences and discuss solutions that are just on the horizon.

1. Stochastic Parrot: The Unreliable Narrator

Present-day LLMs prioritize generating answers over sourcing answers. This approach results in responses that might be accurate or they could be entirely fabricated. Unfortunately, both real and fake answers both are presented with such conviction and structured to look real, that even seasoned experts can be easily misled. This rampant potential for misinformation undermines the credibility of answers provided by Generative AI. In an industry where factual accuracy is paramount, this solution is unusable. Critically interrogating every single detail of a generated response would take more time than simply sourcing an answer from any other established workflow.

2. Lacking an Understanding of the Scientific Complexity

The power of Chat GPT, Bard and Llama, stems from the massive corpus of literature they are trained on. They excel as generalists. However, the language and nuances of biology, chemistry, and life sciences transcend conventional complexities. While these models demonstrate progress in recognizing biological entities, they struggle to capture the intricate web of scientific discourse accurately. Answer to biological questions need to incorporate the nuance of interpreting result and the complex relationships between entities. High level summaries have their place, but details and nuance make or break innovation and success in the life sciences.

3. Lacking Privacy and Data Security

Data within pharmaceutical and life sciences sectors embodies not just invaluable assets but also sensitive patient information. Transmitting such data to uncontrolled servers via APIs raises significant security and ethical concerns. Consequently, many industry players have blacklisted GPT entirely, eliminating any risk of external data leakage and thereby safeguarding proprietary data. Instead, many companies have devoted significant time and money toward exploring how to develop similar technologies internally resulting in a massive redundancy in effort, with little immediate gain.

Envisioning the Future of Generative AI in Life Sciences

For Generative AI to be  truly revolutionary within the life sciences, several major modifications need to be included in their design.

1. Interpreting Domain Specific Documents: Models need to incorporate at their core mastery of the complex written structures and jargon intrinsic to biological researchers.
2. Grasping Foundational Principles: They must comprehend and be founded on the intricate relationships within biology (genes, proteins, drugs, diseases, etc) and with answers conveying a deep understanding of the foundational knowledge known to date.
3. Secure, Local Deployments: Models must balance the power of generalist experts, accessing the extensive biological information in the public domain, while ensuring a secure, proprietary local deployment within an  individual company.
4. Truth and Traceability: Most of all, Generative AI must ensure that every output, every claim, every fact aligns with reality and can be traced back to the evidence used to support that claim.

By embracing these challenges and focusing on refining Generative AI to meet the specific demands and challenges of the life sciences, we pave the way for a future where AI seamlessly integrates into human intuition and efforts to make groundbreaking discoveries and ethical advancements within the field.

Our team is excited to approach these challenges and is excited to work with our clients from strategy to productization, leading the way in the new frontier of AI in Life Sciences.