Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper
Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicated to COVID-19, may yield predictions that differ significantly from those generated using data collected from patients admitted to a high-volume specialized hospital for orthopedic surgery, where COVID-19 is only a secondary diagnosis. This disparity arises despite the two hospitals being geographically close (within20 kilometers). While machine learning can facilitate rapid public health responses, rigorous external validation and continuous monitoring are essential to ensure reliability and safety.
Authors
- Federico Pennestrì (ORCID: https://orcid.org/0000-0001-9969-9955)
- F. Cabitza
- Giuseppe Banfi (ORCID: https://orcid.org/0000-0001-9578-5338)
- N. Picerno
Institutions
- Vita-Salute San Raffaele University (IT)
- University of Milan (IT)
- IRCCS Ospedale Galeazzi - Sant'Ambrogio (IT)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- University of Milano-Bicocca (IT)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-02-04
- DOI
- https://doi.org/10.1186/s12911-025-02883-2
- Citations
- 6
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.93
Funders
- Ministero della Salute