FUTURE-AI: Best practices for trustworthy AI in medical imaging

FUTURE-AI is an international, multi-stakeholder initiative for defining and maintaining concrete guidelines that will facilitate the design, development, validation and deployment of trustworthy AI solutions in medical imaging based on six guiding principles: Fairness, Universality, Traceability, Usability, Robustness and Explainability.

1

Fairness

The first principle of the FUTURE-AI guidelines is the one of Fairness, which states that imaging AI algorithms should maintain...

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2

Universality

While a certain degree of diversity in the design and implementation of AI solutions in medical imaging is both expected and de...

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3

Traceability

The traceability principle states that imaging AI algorithms should be developed together with mechanisms for documenting and m...

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4

Usability

The Usability principle states that the imaging AI solutions should be usable, acceptable and deployable for the end-users in m...

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5

Robustness

The Robustness principle refers to the ability of an imaging AI model to maintain its performance and accuracy when it is appli...

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6

Explainability

The sixth and final principle of the FUTURE-AI guidelines is the one of Explainability, which states that imaging AI algorithms...

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Future AI

Is your image analysis AI tool FUTURE-AI ready?

The FUTURE-AI assessment checklist consists of concrete and actionable questions that will guide developers, evaluators and other stakeholders in delivering imaging AI tools that are trustworthy and optimised for real-world practice.

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Our Partners

University of Barcelona
FORTH
Maastricht University
Instituto de investigacion Sanitaria La Fe
quanticol
Generalitat de Catalunya Departement de Salut
bMaggioli SPA, Research and Development Lab, Athens, Greece
Champalimaud Foundation, Computational Clinical Imaging Group, Lisboa, Portugal
Institute of Information Science and Technologies “Alessandro Faedo” – ISTI