Optimization of Radiology Workflow with Artificial Intelligence

Ranschaert, Erik; Topff, Laurens; Pianykh, Oleg S.

doi:10.1016/j.rcl.2021.06.006

Cited by 41 publications

(15 citation statements)

References 52 publications

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“…If the quality of the data is poor, the quality of the resulting models is also poor. Several methods have been developed for data harmonization, which could significantly impact on radiomic features’ reproducibility [ 104 , 105 , 106 , 107 ].…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Artificial Intelligence in Emergency Radiology: Where Are We Going?

et al. 2022

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Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients’ lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS–PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients’ clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease’s severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.

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Section: Challenges and Perspectivesmentioning

confidence: 99%

Artificial Intelligence in Emergency Radiology: Where Are We Going?

et al. 2022

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“…With ever increasing demand on healthcare systems, and scans remaining a key aspect of care, workflow has surfaced as a key factor. Artificial intelligence is already being explored and even implemented as a tool to facilitate workflow, such as with image acquisition and interpretation 14. Tasking artificial intelligence with these aspects of radiology could free up human radiologists’ time for higher level, complex, interdisciplinary discussions and other aspects of care.…”

Section: The Human Factormentioning

confidence: 99%

Robots, radiologists, and results

Rampton

2022

BMJ

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“…In general, AI applications are predicted to have a profound impact on future radiology [9,10]. Huge efforts are undertaken to establish AI-supported image analysis and interpretation, but also to apply AI to optimize the workflow with respect to operational and financial targets [11,12]. In future, we want to assess how much AI is necessary and useful to reliably collect information from a patient and to achieve a good user experience.…”

Section: Opportunities and Challenges Of Dmia And Cui In Radiologymentioning

confidence: 99%

Digital Medical Interview Assistant for Radiology: Opportunities and Challenges

Denecke

Lombardi

Nairz

2022

Studies in Health Technology and Informatics

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Background: Personal contact between radiologists and their patients is scarce due to time constraints and logistical reasons which impacts on patient knowledgeability and satisfaction, but also on examination and diagnostic quality. Objective: We illuminate medical history interviews from a radiologist’s perspective and discuss its impact on the diagnostic quality. Based on these insights, we develop a digital medical interview assistant (DMIA) for radiology that is intended to collect information helping in improving radiological diagnostics. Methods: Conditions, issues, problems in the radiological examination process are assessed to collect requirements and to specify questions for a digital medical history interview. Results: A DMIA with conversational user interface is developed using the scripting language RiveScript. It is accessible through a social media messenger (Telegram messenger). An initial assessment of usability demonstrates a good usability. Conclusion: To overcome the information gap in radiology, a DMIA can simulate an assessment interview. It is still necessary to remove existing barriers in interaction with the DMIA for example by facilitating data entry options.

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Optimization of Radiology Workflow with Artificial Intelligence

Cited by 41 publications

References 52 publications

Artificial Intelligence in Emergency Radiology: Where Are We Going?

Artificial Intelligence in Emergency Radiology: Where Are We Going?

Robots, radiologists, and results

Digital Medical Interview Assistant for Radiology: Opportunities and Challenges

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