Development and evaluation of a clinical note summarization system using large language models
This advancement underscores the potential of large language models in clinical settings and opens up possibilities for broader applications in healthcare documentation and decision-making support.
Authors
- Manuela Martins Costa (ORCID: https://orcid.org/0000-0003-3492-6795)
- Júlia Couto (ORCID: https://orcid.org/0000-0002-4022-0142)
- Joaquim Santos (ORCID: https://orcid.org/0000-0002-0581-4092)
- Fábio de O. Tabalipa (ORCID: https://orcid.org/0000-0002-2060-953X)
- Juliana Damasio Oliveira (ORCID: https://orcid.org/0000-0001-8915-285X)
- Ana Helena D. P. S. Ulbrich (ORCID: https://orcid.org/0000-0001-6910-8210)
- Rogerio Nunes Nogueira (ORCID: https://orcid.org/0000-0002-4156-8984)
- Henrique Dias Pereira dos Santos (ORCID: https://orcid.org/0000-0002-2410-3536)
- Daniela Faccio
- Marcelo Arocha
Institutions
- Universidade Federal do Rio Grande do Sul (BR)
- Universidade Estadual de Campinas (UNICAMP) (BR)
- Primary HealthCare (MT)
- Engene (Brazil) (BR)
- Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (BR)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-08-28
- DOI
- https://doi.org/10.1038/s43856-025-01091-3
- Citations
- 7
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 4.58
Funders
- Bill and Melinda Gates Foundation