Can one model fit all? Evaluating foundation models for time series forecasting across clinical medicine
- DOI
- 10.1016/j.artmed.2026.103473
- Published
- 2026-10
- Container
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.artmed.2026.103473,
title = {Can one model fit all? Evaluating foundation models for time series forecasting across clinical medicine},
author = {Gernot Pucher and Amin Dada and Aurel Agbodoyetin and Felix Nensa and Martin Schuler and Hans Christian Reinhardt and Jens Kleesiek and Christopher M. Sauer},
year = {2026},
journal = {Artificial Intelligence in Medicine},
doi = {10.1016/j.artmed.2026.103473},
url = {https://doi.org/10.1016/j.artmed.2026.103473}
}RIS
TY - JOUR TI - Can one model fit all? Evaluating foundation models for time series forecasting across clinical medicine AU - Gernot Pucher AU - Amin Dada AU - Aurel Agbodoyetin AU - Felix Nensa AU - Martin Schuler AU - Hans Christian Reinhardt AU - Jens Kleesiek AU - Christopher M. Sauer PY - 2026 JO - Artificial Intelligence in Medicine DO - 10.1016/j.artmed.2026.103473 UR - https://doi.org/10.1016/j.artmed.2026.103473 ER -
APA
Pucher, G., Dada, A., Agbodoyetin, A., Nensa, F., Schuler, M., Reinhardt, H. C., Kleesiek, J., & Sauer, C. M. (2026). Can one model fit all? Evaluating foundation models for time series forecasting across clinical medicine. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2026.103473
Source records
- crossref · retrieved 2026-09-25T20:46:11.602Z