FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2021
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| author | Lekadir, Karim Osuala, Richard Gallin, Catherine Lazrak, Noussair Kushibar, Kaisar Tsakou, Gianna Aussó, Susanna Alberich, Leonor Cerdá Marias, Kostas Tsiknakis, Manolis Colantonio, Sara Papanikolaou, Nickolas Salahuddin, Zohaib Woodruff, Henry C Lambin, Philippe Martí-Bonmatí, Luis |
| author_facet | Lekadir, Karim Osuala, Richard Gallin, Catherine Lazrak, Noussair Kushibar, Kaisar Tsakou, Gianna Aussó, Susanna Alberich, Leonor Cerdá Marias, Kostas Tsiknakis, Manolis Colantonio, Sara Papanikolaou, Nickolas Salahuddin, Zohaib Woodruff, Henry C Lambin, Philippe Martí-Bonmatí, Luis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_09658 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging Lekadir, Karim Osuala, Richard Gallin, Catherine Lazrak, Noussair Kushibar, Kaisar Tsakou, Gianna Aussó, Susanna Alberich, Leonor Cerdá Marias, Kostas Tsiknakis, Manolis Colantonio, Sara Papanikolaou, Nickolas Salahuddin, Zohaib Woodruff, Henry C Lambin, Philippe Martí-Bonmatí, Luis Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2109.09658 |