A review of methods for trustworthy AI in medical imaging: The FUTURE-AI Guidelines

Recent advancements in artificial intelligence (AI) and the vast data generated by modern clinical systems have driven the development of AI solutions in medical imaging, encompassing image reconstruction, segmentation, diagnosis, and treatment planning. Despite these successes and potential, many s...

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Detalles Bibliográficos
Autores: Joshi, Smriti, Martí-Bonmatí, Luis, Woodruff, Henry C., Lekadir, Karim, 1977-, Salahuddin, Zohaib, Tsakou, Gianna, Aussó, Susanna, Cerdá Alberich, Leonor, Papanikolaou, Nickolas, Lambin, Philippe, Marias, Kostas, Kondylakis, Haridimos, Osuala, Richard, Puig Bosch, Xènia, Lazrak, Noussair, Díaz, Oliver, Kushibar, Kaisar, Chouvarda, Ioanna, Charalambous, Stefanie, Starmans, Martijn P. A., Colantonio, Sara, Tsiknakis, Manolis, Tachos, Nikos, Fotiadis, Dimitrios I.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/227393
Acceso en línea:https://hdl.handle.net/2445/227393
Access Level:acceso abierto
Palabra clave:Intel·ligència artificial en medicina
Diagnòstic per la imatge
Imatges mèdiques
Medical artificial intelligence
Diagnostic imaging
Imaging systems in medicine
Descripción
Sumario:Recent advancements in artificial intelligence (AI) and the vast data generated by modern clinical systems have driven the development of AI solutions in medical imaging, encompassing image reconstruction, segmentation, diagnosis, and treatment planning. Despite these successes and potential, many stakeholders worry about the risks and ethical implications of imaging AI, viewing it as complex, opaque, and challenging to understand, use, and trust in critical clinical applications. The FUTURE-AI guideline for trustworthy AI in healthcare was established based on six guiding principles: Fairness, Universality, Traceability, Usability, Robustness, and Explainability. Through international consensus, a set of recommendations was defined, covering the entire lifecycle of medical AI tools, from design, development, and validation to regulation, deployment, and monitoring. In this paper, we describe how these specific recommendations can be instantiated in the domain of medical imaging, providing an overview of current best practices along with guidelines and concrete metrics on how those recommendations could be met, offering a valuable resource to the international medical imaging community.

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