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FAIVOR – a push-button system for AI validation within the hospital

Year: 2025

Type: article

Abstract: One notable challenge for pretrained medical AI models is their validation on new, unseen data. Developers train a model on a limited patient population and users must ensure that they deploy it on a clinical population without any statistical differences. The FAIVOR-tool aims to address these matters by proposing a robust system that enables the evaluation and adaptation of pretrained medical AI systems to new datasets. The tool includes the approach for containerization of AI models, an AI model repository and a tool to fetch and evaluate AI models on the local datasets. These objectives ensure responsible, transparent, and adaptable use of pretrained models in diverse clinical settings.

Source: DOAJ (DOAJ: Directory of Open Access Journals)

DOI: 10.3205/mibe000286

Authors: Slob, Daniël, Akhmad, Ekaterina, Garcia González, Jesus, Amiri, Saba, Kasalica, Vedran, Georgievska, Sonja, Choudhury, Ananya, Lobo Gomes, Aiara, Dekker, Andre, van Soest, Johan

Institutions: Maastricht University Medical Centre, Maastro Clinic, Netherlands eScience Center, Maastricht University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7092290046

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