Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment

Leeuwen, Kicky G. van; Meijer, Frederick J. A.; Schalekamp, Steven; Rutten, M.J.C.M.; Dijk, Ewoud J. van; Ginneken, Bram van; Govers, Tim M.; Rooij, Maarten de

doi:10.1186/s13244-021-01077-4

Cited by 29 publications

(22 citation statements)

References 19 publications

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“…Potential cost savings and the cost-effectiveness of AI applications have been reported recently. 68…”

Section: Tools Of Aimentioning

confidence: 99%

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Hanneman,

Playford,

Dey

et al. 2024

Circulation

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Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

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“…Potential cost savings and the cost-effectiveness of AI applications have been reported recently. 68…”

Section: Tools Of Aimentioning

confidence: 99%

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Hanneman,

Playford,

Dey

et al. 2024

Circulation

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“…At some centers, AI algorithms are being developed to improve patient throughput, increase transportation efficiency, and facilitate visit planning. [20][21][22][23] Similarly, advanced algorithms can be developed to enable not only evidencebased but also local-specific decision support tools that can guide clinicians on when and which diagnostic imaging test to consider for a given patient. For example, the type of cardiac stress test with imaging to order in a given situation may depend not only on patient factors but also on the availability as well as accessibility of a more cost-effective imaging modality.…”

Section: Aimentioning

confidence: 99%

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges

Vrudhula,

Kwan,

Ouyang

et al. 2024

Circ: Cardiovascular Imaging

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Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A’s (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.

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“…This precondition must first be met in order to establish the claim that AI is improving and lowering the cost of healthcare. 21 …”

Section: Role Of Artificial Intelligence As An Aid In Managementmentioning

confidence: 99%

Emerging artificial intelligence-aided diagnosis and management methods for ischemic strokes and vascular occlusions: A comprehensive review

Parvathy,

Kamaraj,

Sah

et al. 2024

World Neurosurgery: X

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Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment

Cited by 29 publications

References 19 publications

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges

Emerging artificial intelligence-aided diagnosis and management methods for ischemic strokes and vascular occlusions: A comprehensive review

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