Addressing the translation gap in SCT donor selection
David Higgins, Madlen Reschke, Jonas Seiler, Jonathan P. Gross, Lena Oevermann · 2026
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Description
David Higgins and co-authors offer a 2026 practitioner's view on why machine learning donor selection for stem cell transplants stalls before reaching clinics. Models keep improving on paper, yet wards barely use them, so the team draws lessons from a federally funded Berlin incubation project run from 2021 to 2023.
Registry data, they argue, was never built for learning algorithms and needs rethinking from the ground up, with shared multi-center infrastructure as a prerequisite. Teams should gather broader clinical measures and test tools in forward-looking studies tied to standard care. The authors also warn about regulatory load, true resource costs, and market incentives that quietly block adoption.
Catalog record
| CREATOR | David Higgins, Madlen Reschke, Jonas Seiler, Jonathan P. Gross, Lena Oevermann |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 20840 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |