Quantifying Epistemic Uncertainty in Predictions for Safer Health AI Performance Under Dataset Shifts.

Fernández-Narro D, Ferri P, García-Gómez JM, Sáez C

DOI: 10.3233/shti251493
Published: 2025 Oct 2
Container: Studies in health technology and informatics
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3233/shti251493,
  title = {Quantifying Epistemic Uncertainty in Predictions for Safer Health AI Performance Under Dataset Shifts.},
  author = {Fernández-Narro D and Ferri P and García-Gómez JM and Sáez C},
  year = {2025},
  journal = {Studies in health technology and informatics},
  doi = {10.3233/shti251493},
  url = {https://doi.org/10.3233/shti251493}
}

RIS

TY  - JOUR
TI  - Quantifying Epistemic Uncertainty in Predictions for Safer Health AI Performance Under Dataset Shifts.
AU  - Fernández-Narro D
AU  - Ferri P
AU  - García-Gómez JM
AU  - Sáez C
PY  - 2025
JO  - Studies in health technology and informatics
DO  - 10.3233/shti251493
UR  - https://doi.org/10.3233/shti251493
ER  -

APA

D, F., P, F., JM, G., & C, S. (2025). Quantifying Epistemic Uncertainty in Predictions for Safer Health AI Performance Under Dataset Shifts.. Studies in health technology and informatics. https://doi.org/10.3233/shti251493

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pubmed · retrieved 2026-09-25T13:41:36.552Z