Generative AI costs in large healthcare systems, an example in revenue cycle.

TitleGenerative AI costs in large healthcare systems, an example in revenue cycle.
Publication TypeJournal Article
Year of Publication2025
AuthorsBurns ML, Chen S-Y, Tsai C-A, Vandervest J, Pandian B, Nong P, Hanauer DA, Rosenberg A, Platt J
JournalNPJ Digit Med
Volume8
Issue1
Pagination579
Date Published2025 Sep 30
ISSN2398-6352
Abstract

Application of large language models in healthcare continues to expand, specifically for medical free-text classification tasks. While foundation models like those from ChatGPT show potential, alternative models demonstrate superior accuracy and lower costs. This study underscores significant challenges, including computational costs and model reliability. Amidst rising healthcare expenditures and AI's perceived potential to reduce costs, a combination of local and commercial models might offer balanced solutions for healthcare systems.

DOI10.1038/s41746-025-01971-x
Alternate JournalNPJ Digit Med
PubMed ID41028226
PubMed Central IDPMC12485018
Grant ListR01 EB030492 / EB / NIBIB NIH HHS / United States
1-RO1-EB030492 / / National Institutes of Health, The National Institute of Biomedical Imaging and Bioengineering (NIBIB), Public Trust of Artificial Intelligence in the Precision CDS Health Ecosystem /