mlsanityData to 7 Oct 2026

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

arXiv 2109.09658 ↗First posted 20 Sep 2021cs.CV cs.AI cs.LGcited by 13

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Abstract (arXiv)

The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Addressing these concerns and risks, the FUTURE-AI framework has been proposed, which, sourced from a global multi-domain expert consensus, comprises guiding principles for increased trust, safety, and adoption for AI in healthcare. In this paper, we transform the general FUTURE-AI healthcare principles to a concise and specific AI implementation guide tailored to the needs of the medical imaging community. To this end, we carefully assess each building block of the FUTURE-AI framework consisting of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability, and respectively define concrete best practices based on accumulated AI implementation experiences from five large European projects on AI in Health Imaging. We accompany our concrete step-by-step medical imaging development guide with a practical AI solution maturity checklist, thus enabling AI development teams to design, evaluate, maintain, and deploy technically, clinically and ethically trustworthy imaging AI solutions into clinical practice.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 47 of its 218 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

47 of 218 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 2; 2024: 4; 2025: 3; 2026: 1.

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  3. 2023Reproducibility of Machine Learning: Terminology, Recommendations and Open Issuescited by 8
  4. 2022Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classificationcited by 7
  5. 2022COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformerscited by 4
  6. 2024Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systemscited by 3
  7. 2025DURA-CPS: A Multi-Role Orchestrator for Dependability Assurance in LLM-Enabled Cyber-Physical Systemscited by 3
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  9. 2024A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimagingcited by 2
  10. 2025Robust Training with Data Augmentation for Medical Imaging Classificationcited by 1
  11. 2025RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosiscited by 1
  12. 2024AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Reviewcited by 0
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  1. 2026From Awareness to Action: Understanding and Overcoming the Research-Practice Gap in Algorithmic Fairness for Public Healthcited by 0