Navigating fairness aspects of clinical prediction models
We aim to direct healthcare professionals on navigating the complexities of bias in healthcare algorithms by encouraging critical thinking about biases present in society, data, algorithms, and healthcare systems.
Authors
- Tri‐Long Nguyen (ORCID: https://orcid.org/0000-0002-6376-7212)
- Sedrah B. Balaganeshan (ORCID: https://orcid.org/0000-0002-0275-7440)
- Katrina Chakradeo (ORCID: https://orcid.org/0000-0001-6091-5948)
- Ádám Hulmán (ORCID: https://orcid.org/0000-0002-3969-1000)
- Mikkel Malham (ORCID: https://orcid.org/0000-0002-6956-7676)
- Tibor V. Varga (ORCID: https://orcid.org/0000-0002-2383-699X)
- Ole L. Dollerup (ORCID: https://orcid.org/0000-0001-6924-7114)
- Inchuen Huynh
- Hjørdis Gade-Jørgensen
- Susanne K. Laupstad
Institutions
- University of Copenhagen (DK)
- Steno Diabetes Centers (DK)
- Aarhus University (DK)
- Hvidovre Hospital (DK)
- Aarhus University Hospital (DK)
- University College Copenhagen (DK)
- Capital Region of Denmark (DK)
Publication Details
- Journal
- BMC Medicine
- Published
- 2025-10-17
- DOI
- https://doi.org/10.1186/s12916-025-04340-3
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.27