FHIR-Former: enhancing clinical predictions through Fast Healthcare Interoperability Resources and large language models
By harmonizing FHIR standardization with LLM flexibility, FHIR-Former advances scalable, interoperable predictive modeling in healthcare. The open-source framework facilitates automation, improves resource allocation, and supports personalized decision-making, bridging gaps between AI innovation and clinical practice.
Authors
- Amin Dada (ORCID: https://orcid.org/0000-0003-4016-7799)
- Jens Kleesiek (ORCID: https://orcid.org/0000-0001-8686-0682)
- Giulia Baldini (ORCID: https://orcid.org/0000-0002-5929-0271)
- Merlin Engelke (ORCID: https://orcid.org/0000-0002-8463-8786)
- Felix Nensa (ORCID: https://orcid.org/0000-0002-5811-7100)
Institutions
- TU Dortmund University (DE)
- Essen University Hospital (DE)
- Artificial Intelligence in Medicine (Canada) (CA)
- University of Duisburg-Essen (DE)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-09-16
- DOI
- https://doi.org/10.1093/jamia/ocaf165
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 9.92
Funders
- Universitätsklinikum Essen
- Universität Duisburg-Essen