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Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making

Crossref DOI link: https://doi.org/10.1186/s12874-025-02703-1

Published Online: 2025-12-31

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Rizzo, Matteo https://orcid.org/0000-0002-9723-211X
Veneri, Alberto

Marcuzzo, Matteo

Zangari, Alessandro

Albarelli, Andrea

Lucchese, Claudio

Nobile, Marco Salvatore

Conati, Cristina
Funding

Funding for this research was provided by:

European Commission (NRRP DM 352/2022)

European Commission (“Interconnected Nord-Est Innovation Ecosystem (iNEST)” project and received funding from the European Union Next-GenerationEU - National Recovery and Resilience Plan (NRRP) — MISSION 4 COMPONENT 2, INVESTIMENT N. ECS00000043 — CUP N. H43C22000540006?)
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Text and Data Mining valid from 2025-12-31

Version of Record valid from 2025-12-31
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Article History

Received: 29 February 2024

Accepted: 6 October 2025

First Online: 31 December 2025

Declarations

:

: Not applicable.

: Not applicable.

: The authors declare that they have no competing interests.

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