In recent months, ChatGPT has taken the internet by storm, leading many people to ask how it will change their industry, what it can be used for, and whether their jobs are safe. Our perspective as a company focused on rebuilding medical affairs using language models is that ChatGPT and deep learning language models broadly will have a significant impact in the following 3 ways:
- Facilitating Research and Content Generation:
Medical Affairs Professionals (MAPs) spend a significant amount of time researching and gathering information. However, deep learning models like ChatGPT can analyze vast amounts of data in a matter of minutes, helping MAPs to quickly and easily access the information they need. This means more time for meaningful engagement with healthcare professionals and less time spent sifting through mountains of data.
And it can even go further than that. As of last week, ChatGPT has been credited as a co-author on at least four published papers in scientific journals, according toNature. Additionally, it’s not just random general information, an Open AI bot recently passed the US medical licensing exam, according to Futurism.
In response to this shifting environment, the World Association of Medical Editors (WAME) published a position paper on Chatbots saying that they cannot be authors and that human authors are responsible for their content.
Regardless of who owns the content, the speed and power of this tool, especially when trained on medical ontologies and data, will transform research. And in a research-intensive field like medical affairs, the impact will be particularly acute.
- Eliminating Data Silos:
Deep learning language models can be programmed to provide real-time information on the latest medical trends and advancements, helping MAPs stay up-to-date and informed. This means that MAPs can bring the most current information to their engagements from both internal and external data sources, providing value to healthcare professionals and positioning themselves as experts in their field.
Every organization has its own data storage methods and internal processes. But the organizations that change how they are processing their data internally will have a monumental advantage. This will be particularly important as the world of scientific engagement and discovery is brought into hyperdrive with AI.
According to a recent study it is estimated that by 2030, the AI healthcare market will reach a value of$15.4 billion growing at a 37.5% CAGR. Regardless of MAPs’ therapeutic area, understanding generative AI’s role in processing data is essential.
We can already see the use case for it in other biotech arenas for example generative AI is becoming the foundationalpillar of drug development and discovery. Having an AI infrastructure layer will be critical to building productive medical affairs teams in the near future.
- Streamlining Workflows:
Deep learning models can automate many administrative tasks, freeing up MAPs to focus on what they do best: engaging with healthcare professionals. From appointment scheduling to data analysis, these models can handle it all. This leads to more efficient and productive workflows, allowing MAPs to make the most of their time.
If you want to see the future of these workflow changes in action, think about why Microsoft invested $10 Billion dollars into ChatGPT earlier this month. Within weeks of this purchase, Microsoft announced that they would release Microsoft Teams Premium, which employs OpenAI’s ChatGPT technology to take notes during meetings and organize tasks afterward. Basically, this tool will supercharge meetings by automating note-taking during the meeting and facilitating administrative tasks like follow-up emails, calendar invites, and work plans afterward. The implications could be broader, improving KOL interactions, personalized HCP follow-ups, and even improving insight generation across Medical Affairs teams. Imagine what a solution directly tailored for medical affairs and trained on medical data could do with this power.
In conclusion, ChatGPT and deep learning language models as a whole are changing the way MAPs work, streamlining workflows, providing valuable insights, and enabling effective engagement with healthcare professionals. We at DocNexus are excited to be part of this generative landscape and look forward to showing you how we fit into it.
Summary: MBA/MPP from Duke University and Co-Founder of DocNexus. DocNexus is a next-generation infrastructure layer for medical affairs—helping to streamline workflows and generate insights to get life-saving products to market faster.
Author:
Gordon Silverman, MBA/MPP
Gordon grew up in Phoenix, Arizona and early in his career had dreams of becoming a public servant following in the footsteps of his family in helping to desegregate the South. After spending a summer at Harvard’s Center for International Development working on a big data tool called the Atlas of Economic Complexity, Gordon learned how harnessing big data, in this case, country-level trade data, could change the world. Since then, Gordon went on to pursue an MBA/MPP from Duke and along the way has worked in strategy, innovation, design, and big data. Working in everything from the government in institutions like the International Finance Corporation of the World Bank (where he worked on creating the big data tool (Aimm Framework) used to evaluate the impact and financial returns for all projects) to the White House to a design and strategy shop called Frog Design. With experience working in big data tools like the Atlas and the AIMM framework, Gordon believes DocNexus will change the landscape of healthcare data forever.
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