Current challenges of implementing artificial intelligence in medical imaging

Saw, Shier Nee; Ng, Kwan Hoong

doi:10.1016/j.ejmp.2022.06.003

Cited by 28 publications

(14 citation statements)

References 56 publications

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“…It offers many incentives for developing AI models. However, there are several ethical and legal concerns, before AI can be used with full confidence in clinical practice [107] [108]. Around the world, regulatory agencies issued guidelines to help define best practices for AI development and deployment.…”

Section: Discussionmentioning

confidence: 99%

Predictive value of clot imaging in acute ischemic stroke: A systematic review of artificial intelligence and conventional studies

LaGrange

Hofmeister²,

Rosi³

et al. 2023

Neuroscience Informatics

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Section: Discussionmentioning

confidence: 99%

Predictive value of clot imaging in acute ischemic stroke: A systematic review of artificial intelligence and conventional studies

LaGrange

Hofmeister²,

Rosi³

et al. 2023

Neuroscience Informatics

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“…Saw et al. identified four overarching challenges for effectively implementing medical imaging AI/ML: data governance, algorithm robustness, stakeholder consensus, and legal liability 67 . While many of these challenges are intimately part of an AI/ML device review (data governance, algorithm robustness, and performance assessment), others, such as legal liability, generally fall outside of FDA’s purview.…”

Section: Discussionmentioning

confidence: 99%

“…While many of these challenges are intimately part of an AI/ML device review (data governance, algorithm robustness, and performance assessment), others, such as legal liability, generally fall outside of FDA’s purview. Data governance concerns relate to developing effective policies and protocols for storing, securing, and maintaining data quality, including images, metadata, and reference standard (truth) labels 67 . Algorithm robustness concerns generally include how to reduce algorithmic bias and improve fairness across patients, groups, and sites.…”

Section: Discussionmentioning

confidence: 99%

“…FDA is particularly concerned with how sponsor studies, often based on limited patient, group and site diversity, generalize to actual clinical practice across the United States. All AI/ML algorithms are biased to some extent because the data used to train the models are intrinsically a function of the population groups, disease conditions, clinical environments, and imaging technologies available during the collection process 67 . Therefore, robustness includes not only developing methods to measure and reduce important sources of bias but also defining fairness criteria for the AI/ML under appropriate operational conditions as well as integrating the appropriate level of transparency.…”

Section: Discussionmentioning

confidence: 99%

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Regulatory considerations for medical imaging AI/ML devices in the United States: concepts and challenges

et al. 2023

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To introduce developers to medical device regulatory processes and data considerations in artificial intelligence and machine learning (AI/ML) device submissions and to discuss ongoing AI/ML-related regulatory challenges and activities.Approach: AI/ML technologies are being used in an increasing number of medical imaging devices, and the fast evolution of these technologies presents novel regulatory challenges. We provide AI/ML developers with an introduction to U.S. Food and Drug Administration (FDA) regulatory concepts, processes, and fundamental assessments for a wide range of medical imaging AI/ML device types. Results:The device type for an AI/ML device and appropriate premarket regulatory pathway is based on the level of risk associated with the device and informed by both its technological characteristics and intended use. AI/ML device submissions contain a wide array of information and testing to facilitate the review process with the model description, data, nonclinical testing, and multi-reader multi-case testing being critical aspects of the AI/ML device review process for many AI/ML device submissions. The agency is also involved in AI/ML-related activities that support guidance document development, good machine learning practice development, AI/ML transparency, AI/ML regulatory research, and real-world performance assessment.Conclusion: FDA's AI/ML regulatory and scientific efforts support the joint goals of ensuring patients have access to safe and effective AI/ML devices over the entire device lifecycle and stimulating medical AI/ML innovation.

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“…Currently, the Food and Drug Administration keeps a database of AI software and systems that have been approved for use in clinical settings [ 3 ]. In various facets within the realm of AI in radiology, there are still challenges and hurdles to overcome, including ethical issues, algorithm robustness, data governance, stakeholders’ consensus, and legal liability [ 4 ].…”

mentioning

confidence: 99%

Current challenges of implementing artificial intelligence in medical imaging

Cited by 28 publications

References 56 publications

Predictive value of clot imaging in acute ischemic stroke: A systematic review of artificial intelligence and conventional studies

Predictive value of clot imaging in acute ischemic stroke: A systematic review of artificial intelligence and conventional studies

Regulatory considerations for medical imaging AI/ML devices in the United States: concepts and challenges

It is Time to Incorporate Artificial Intelligence in Radiology Residency Programs

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