Utility of word embeddings from large language models in medical diagnosis.

ABSTRACT:

Objective

This study evaluates the utility of word embeddings, generated by large language models (LLMs), for medical diagnosis by comparing the semantic proximity of symptoms to their eponymic disease embedding ("eponymic condition") and the mean of all symptom embeddings associated with a disease ("ensemble mean").

Materials and methods

Symptom data for 5 diagnostically challenging pediatric diseases-CHARGE syndrome, Cowden disease, POEMS syndrome, Rheumatic fever, and Tuberous sclerosis-were collected from PubMed. Using the Ada-002 embedding model, disease names and symptoms were translated into vector representations in a high-dimensional space. Euclidean and Chebyshev distance metrics were used to classify symptoms based on their proximity to both the eponymic condition

SUBMITTER: Yazdani S 

PROVIDER: S-EPMC11833464 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature