V A L I D A T E - Validation of a Trustworthy AI-based Clinical Decision Support System for Improving Patient Outcome in Acute Stroke Treatment

Informazioni relative al progetto

VALIDATE

ID dell’accordo di sovvenzione: 101057263

DOI

10.3030/101057263

Progetto chiuso

Data della firma CE 23 Maggio 2022

Data di avvio 1 Maggio 2022

Data di completamento 30 Aprile 2026

Finanziato da

• Health

Costo totale

€ 5 918 175,00

Contributo UE

€ 5 918 175,00

Coordinato da

CHARITE - UNIVERSITAETSMEDIZIN BERLIN
Germany

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (17)

Z-inspection process result report
Report of the standard treatment scenario based on current care pathways and SoC operating procedures
Midterm recruitment report
VALIDATE trustworthy AI framework and manual
Models and report on Iteration 1, transition from TRL 3 to 4
Report of qualitative interview study
Models and report on Iteration 2, transition from TRL 4 to 5
Research data management Plan (DMP)
Audit results, framework and manual update at M36
Cost-effectiveness and budget-impact framework
Study initiation package
Project management plan
Integrated requirements report covering technical and user requirements
Communication and dissemination strategy
Completion of pre-clinical study (retrospective)
Audit results, framework and manual update at M18
Electronic patient recorded outcome framework (NORA) adaptation

Pubblicazioni

Peer reviewed articles (5)

Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review

Autori: Luis Miguel Lopez Ramos, Florian Leiser, Aditya Rastogi, Steven Hicks, Inga Str ̈umke, Vince I. Madai, Tobias Budig, Ali Sunyaev, and Adam Hilbert
Pubblicato in: IEEE Transactions on Knowledge and Data Engineering, 2024, ISSN 1041-4347
Editore: IEEE Educational Activities Department
DOI: 10.48550/ARXIV.2411.05874

Explainability of machine learning models under missing data

Autori: Tuan L. Vo, Thu Nguyen, Luis M. Lopez-Ramos, Hugo L. Hammer, Michael A. Riegler, Pål Halvorsen
Pubblicato in: Applied Soft Computing, Numero 196, 2026, ISSN 1568-4946
Editore: Elsevier BV
DOI: 10.1016/J.ASOC.2026.115105

Co-design of a trustworthy AI-based prognostic tool for predicting patient outcome in acute stroke

Autori: Elizabeth Hofvenschioeld, Adam Hilbert, Cathrine K. T. Bui, Susanne Bonekamp, Tess Buckley, Roberta Calegari, Luca A. Cappellini, Megan Coffee, Giorgio Colangelo, Boris Düdder, Francesco Di Tano, Dietmar Frey, John D. Kelleher, Pedro Kringen, Andreane Sabourin Laflamme, Emma Moorhead, Pedro A. Moreno-Sánchez, Gabriele Pluktaite, Marta Rubiera, Giovanni Sartor, Jesmin Jahan Tithi, Malte von Tottleben, Ingo Werren, Magnus Westerlund, Renee Wurth, Roberto V. Zicari, Vince I. Madai
Pubblicato in: AI and Ethics, Numero 6, 2026, ISSN 2730-5953
Editore: Springer Science and Business Media LLC
DOI: 10.1007/S43681-026-01337-3

Operationalizing AI ethics in medicine—a co-creation workshop study

Autori: Luis M. Lopez-Ramos, Gabriele Pluktaite, Cathrine K. T. Bui, Julia Amann, Tamarinde Haven, Vince I. Madai, null null
Pubblicato in: BMC Medical Ethics, Numero 26, 2025, ISSN 1472-6939
Editore: Springer Science and Business Media LLC
DOI: 10.1186/S12910-025-01317-Y

How ethics operationalization breaks down in practice: expert perspectives from healthcare AI

Autori: Riana Minocher, Isabel Velarde, Valerie Buerger, Gabriele Pluktaite, Vince I. Madai
Pubblicato in: AI and Ethics, Numero 6, 2026, ISSN 2730-5953
Editore: Springer Science and Business Media LLC
DOI: 10.1007/S43681-026-01381-Z