Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics

Létourneau-Guillon, Laurent; Camirand, David; Guilbert, F; Forghani, Reza

doi:10.1016/j.nic.2020.08.008

Cited by 37 publications

(24 citation statements)

References 81 publications

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“…Moreover, the demand for the specialized evaluation of x-rays usually exceeds the available number of radiologists 4 . The use of Machine Learning (ML) algorithms to support clinical decisions has become increasingly popular in various radiology contexts 5 , 6 : workflow optimization 7 , detecting relevant imaging alterations to support disease diagnosis 8 , and also automated generation of radiology reports 9 – 11 . These solutions can be especially useful in underdeveloped regions and communities where there is a shortage of radiologists 12 .…”

Section: Background and Summarymentioning

confidence: 99%

BRAX, Brazilian labeled chest x-ray dataset

et al. 2022

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Chest radiographs allow for the meticulous examination of a patient’s chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) algorithms. Large labeled datasets are key elements for training and validation of these ML solutions. In this paper we describe the Brazilian labeled chest x-ray dataset, BRAX: an automatically labeled dataset designed to assist researchers in the validation of ML models. The dataset contains 24,959 chest radiography studies from patients presenting to a large general Brazilian hospital. A total of 40,967 images are available in the BRAX dataset. All images have been verified by trained radiologists and de-identified to protect patient privacy. Fourteen labels were derived from free-text radiology reports written in Brazilian Portuguese using Natural Language Processing.

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Section: Background and Summarymentioning

confidence: 99%

BRAX, Brazilian labeled chest x-ray dataset

et al. 2022

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“…ML algorithms for clinical workflow integrations have been studied extensively in the past years with multiple authors suggesting different applications [ 11 , 12 , 52 , 54 , 55 ]. Olthof et al suggest that radiologist workflows could be supported, extended, or replaced by ML functionalities [ 56 ].…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, machine learning (ML) has been increasingly applied in neuroimaging to alleviate some of these challenges using automated workflow improvements [ 11 ]. A potential application of ML algorithms could be to automate scan-sequence acquisition alterations based on real-time image analysis while the patient is still in the scanner [ 12 ]. Another application could be to improve scan interpretation efficiencies by prioritizing the reading list of essential and acute/critical findings [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

et al. 2022

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We conducted a systematic review of the current status of machine learning (ML) algorithms’ ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study’s design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this.

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“…Second, in cases where cost of care may increase, this has to be offset by substantial increases in operational efficiency. 21 In the second case, the resource-based relative value scale may require adjustment, since the effect of the AI offsets the decrease in effort. In addition to the three criteria specified by CMS in the NTAP regulations (i.e.…”

Section: Strategies For Future Stroke Ai Platformsmentioning

confidence: 99%

Insurance payment for artificial intelligence technology: Methods used by a stroke artificial intelligence system and strategies to qualify for the new technology add-on payment

et al. 2022

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The first ever insurance reimbursement for an artificial intelligence (AI) system, which expedites triage of acute stroke, occurred in 2020 when the Centers for Medicare and Medicaid Services (CMS) granted approval for a New Technology Add-on Payment (NTAP). Key aspects of the AI system that led to its approval by the CMS included its unique mechanism of action, use of robotic process automation, and clear linkage of the system’s output to clinical outcomes. The specific strategies employed encompass a first-case scenario of proving reimbursable value for improved stroke outcomes using AI. Given the rapid change in utilization of AI technology in stroke care, we describe the economic drivers of stroke AI systems in healthcare, focusing on concepts of reimbursement for value added by AI to the stroke care system. This report reviews (1) the successful approach used by the first NTAP-approved AI system, (2) economic variables in insurance reimbursement for AI, and (3) resultant strategies that may be utilized to facilitate qualification for NTAP reimbursement, which may be adopted by other AI systems used in stroke care.

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Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics

Cited by 37 publications

References 81 publications

BRAX, Brazilian labeled chest x-ray dataset

BRAX, Brazilian labeled chest x-ray dataset

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

Insurance payment for artificial intelligence technology: Methods used by a stroke artificial intelligence system and strategies to qualify for the new technology add-on payment

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