How AI Can Make Clinical Rheumatology Research More Efficient & Fruitful

Dr. Park: For me, there are several practical barriers to integrating LLMs into my work: 1) acquiring and consolidating massive amounts of EHR text data (like clinical notes) from multiple resources; 2) collaborating with the right data science/bioinformatics/AI engineering partners to pre-process and format such data so they can be fit to use for LLMs; and 3) ensuring Protected Health Information (PHI) data are safeguarded under institutional guidelines and working within that framework when using open-source LLM tools.

TR: For physicians and researchers curious about AI but not well versed in its details, what do you recommend they do to learn more about this topic?

Dr. Park: It’s a rapidly changing landscape, so it requires a lot of upkeep, both at your own institution as well as in the literature. For rheumatology researchers who are interested, I think you first have to come up with a good applicable clinical inquiry and make sure you are partnered with the right data science/bioinformatics partners who are equipped with technical capacity to use LLMs. Of course, you must also ensure you are working within the framework of your institutional policies. I encourage everyone reading this article to seek to learn more about this exciting area.

Dr. Jason Liebowitz

Jason Liebowitz, MD, FACR, is an assistant professor of medicine in the Division of Rheumatology at Columbia University Vagelos College of Physicians and Surgeons, New York.