Deep learning/artificial intelligence concepts have been around for decades, with significant advancements made in the late ‘80s and throughout the ‘90s as scientists experimented with recurring neural networks (RNNs).
As our concept and expectations of deep learning evolved over the years, data quality and the computing power needed to train and deploy new models often hindered advancements. Graphic processing units (GPUs) have played a significant role in our ability to overcome this computational challenge which has led to many achievements in recent years.
Transformers, or large language models (LLMs), first introduced by Google in 2017 opened the door to how we solve some of the biggest challenges facing data and analytics – particularly around the analysis of complex unstructured documents. And in 2023, the AI world flipped on its head with OpenAI’s release of ChatGPT.
GPTs (Generative Pretrained Transformers) & the AI Revolution
Instead of being seen as just an innovative tool for science and research, user-friendly ChatGPT brought AI into the mainstream. In fact, it’s the fastest app to reach 1 million users which it achieved in 5 days. For context, that’s 70 days faster than Instagram and 145 days faster than Spotify – two enormously popular apps that are likely on your phone as you read this blog.
Beyond just being an incredibly powerful tool, the accessibility of ChatGPT has changed expectations for how we use AI. In many cases, people were used to interacting with AI via complex coding languages or use-case specific apps. However, the chatbot functionality and interaction of ChatGPT made AI approachable for non-programmers and reinvigorated interest in its use across industries, including life sciences.
This evolution has created a cascade of new AI models entering the market. And with each model that is deployed, the size of the model and the data it’s trained on grow exponentially, enabling the model to comprehend even greater amounts of information. For example, Llama 2 from Meta is trained on 70B parameters and Falcon just released a 180B parameter model meaning they’ve been trained on even greater amounts of data and thus even more powerful than previous releases.
As these models get larger, they begin to learn incredibly fast and are quickly surpassing human benchmarks making it not a matter of if, but when AI can be used to enhance your work.
The Problem with Generalized GPTs
While the growth of GPTs is incredibly promising, the life sciences industry continues to face challenges with nearly 73% of companies struggling to adopt appropriate AI technologies.
One key barrier to adoption is that GPTs by nature have flaws when operating independently. For example, most GPTs- including the most popularly used one, ChatGPT- are trained on broad sets of information (in many cases Wikipedia) which makes them great at suggesting attractions to visit on a vacation, but they fall short when attempting to answer complex life science questions. This scenario largely comes down to a data accessibility and training issue where the model has “too general” knowledge, much of which may be outdated. As a result, these features impede the successful implementation of GPTs in life sciences, particularly drug R&D.
So, to answer the question asked at the beginning of this blog, “No, ChatGPT is likely not an ideal technology for your life sciences organization.”
Making GPTs a Success in Life Sciences
At Certara, we believe that combining the appropriate data architecture with specialized GPTs will solve this issue. Our solution, Certara.AI combines GPT models pre-trained on life sciences data with a flexible data connectivity platform enabling GPT access to organizational data in real-time.
As a result, life science organizations can securely deploy specialized AI applications that meet their rigorous standards without having to reconfigure their existing data environment. This approach not only enables rapid, trusted AI deployments, but it also provides flexible GPT deployment across various use cases in the organization.
Enhancing Drug R&D with GPTs
Certara.AI contains exciting integrations that meet specific use cases across the drug development life cycle. For example:
Drug Discovery: Understanding a molecule’s viability for further development requires analyzing complex chemical, biological, and computational data. Certara.AI can comprehend complex data types so discovery scientists spend less time running mundane computational analyses and research tasks. For example, Certara.AI’s library of property prediction models enables discovery scientists to more quickly prioritize the most viable chemical structures for their R&D tasks allowing them to spend less time on time-consuming calculations.
Clinical Trial Benchmarking: The success rate of clinical trials has not improved in decades, which is a huge waste of time and resources. Designing a trial with the best probability of success requires fully understanding the existing data in the field. However, 80% of the data clinical trial teams need resides in unstructured content that isn’t amenable to conventional analysis. When trained in biomedical literature, Certara.AI reduces manual research tasks and accelerates collecting the relevant data teams need to design and inform successful trial execution.
Medical Writing: Medical writers spend a tremendous amount of time manually developing document drafts for regulatory submissions. 有了人工智能生成的内容，医学撰写人就能简化早期起草流程，从而有更多时间对内容进行微调和质量控制，使监管文件的开发更富有成效。
While the era of GPTs is still in its infancy, addressing its shortcomings and accelerating early deployment will be critical to long-term successful adoption. Certara.AI is a flexible solution that was developed by industry experts. It comprises the foundation that life science organizations need for successful AI and GPT implementation. Interested in learning more? Click here to set up a meeting with the Certara.AI team.
