Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system
Taken together, the developed integration framework through the use of the HL7 standard and freely available DP resources offers a standardized, portable, and open-source solution that lays the groundwork for the future widespread adoption of DL models in pathology diagnostics.
Authors
- Miriam Angeloni (ORCID: https://orcid.org/0000-0003-2105-6305)
- Arndt Hartmann (ORCID: https://orcid.org/0000-0002-4860-4626)
- Davide Rizzi
- Francesco Merolla (ORCID: https://orcid.org/0000-0003-0711-6738)
- Filippo Fraggetta (ORCID: https://orcid.org/0000-0002-0040-9987)
- Alessandro Caputo (ORCID: https://orcid.org/0000-0001-5139-3869)
- Simon Schoen
- Fulvia Ferrazzi (ORCID: https://orcid.org/0000-0003-4011-4638)
Institutions
- University of Molise (IT)
- University of Salerno (IT)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Comprehensive Cancer Center Erlangen (DE)
Publication Details
- Journal
- Genome Medicine
- Published
- 2025-05-26
- DOI
- https://doi.org/10.1186/s13073-025-01484-y
- Citations
- 8
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 19.85
Funders
- Deutsche Forschungsgemeinschaft
- Universitätsklinikum Erlangen