LLMonFHIR
LLMonFHIR's ability to generate responses in multiple languages and at varying levels of complexity, along with its bidirectional text-to-speech functionality, give it the potential to empower individuals with limited functionality, English, and health literacy to access the benefits of patient-accessible EHRs.
Authors
- Adrit Rao (ORCID: https://orcid.org/0000-0002-0780-033X)
- Arash Fereydooni (ORCID: https://orcid.org/0000-0002-2519-5137)
- Lauren Aalami (ORCID: https://orcid.org/0009-0007-7132-5362)
- Paul Schmiedmayer (ORCID: https://orcid.org/0000-0002-8607-9148)
- Aydin Zahedivash (ORCID: https://orcid.org/0000-0001-6835-1139)
- Vishnu Ravi (ORCID: https://orcid.org/0000-0003-0359-1275)
- Oliver Aalami (ORCID: https://orcid.org/0000-0002-7799-2429)
- Philipp Zagar (ORCID: https://orcid.org/0009-0001-5934-2078)
- D Yao (ORCID: https://orcid.org/0009-0006-1145-9329)
Institutions
- Stanford Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- JACC Advances
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1016/j.jacadv.2025.101780
- Citations
- 4
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 3.22