IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
Document Type
Poster
Publication Date
Spring 2025
Abstract
Pediatric rheumatology is an understudied and underprovided field in medicine. Currently, there are only 850 pediatric rheumatologists in the United States (1). This can make it incredibly difficult to get a diagnosis, since pediatric rheumatologic disorders are rare and thus easily missed. For example, eight states have no pediatric rheumatologists at all, and only 25% of children with arthritis are able to see a rheumatologist (2). AI applications in pediatric rheumatology are lacking, likely due to the combination of a lack of interest and a lack of data. This project utilizes a large language model (LLM) to create an AI model that accurately diagnoses 9 pediatric rheumatological disorders– or the absence thereof– given input symptomatology and/or test results. This could be utilized in places without access to pediatric rheumatologists to ensure early and accurate patient diagnosis.
Recommended Citation
Lowe, Juliette, "BioLinkBERT-PR: A Large Language Model for Diagnosing Pediatric Rheumatological Disorders" (2025). IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound. Paper 53.
https://digital.kenyon.edu/dh_iphs_ai/53
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.