Journal of the American College of Radiology : JACR
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Within artificial intelligence, machine learning (ML) efforts in radiation oncology have augmented the transition from generalized to personalized treatment delivery. Although their impact on quality and safety of radiation therapy has been limited, they are increasingly being used throughout radiation therapy workflows. Various data-driven approaches have been used for outcome prediction, CT simulation, clinical decision support, knowledge-based planning, adaptive radiation therapy, plan validation, machine quality assurance, and process quality assurance; however, there are many challenges that need to be addressed with the creation and usage of ML algorithms as well as the interpretation and dissemination of findings. In this review, the authors present current applications of ML in radiation oncology quality and safety initiatives, discuss challenges faced by the radiation oncology community, and suggest future directions.
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Recent advances in machine learning and artificial intelligence offer promising applications to radiology quality improvement initiatives as they relate to the radiology value network. Coordination within the interlocking web of systems, events, and stakeholders in the radiology value network may be mitigated though standardization, automation, and a focus on workflow efficiency. In this article the authors present applications of these various strategies via use cases for quality improvement projects at different points in the radiology value network. In addition, the authors discuss opportunities for machine-learning applications in data aggregation as opposed to traditional applications in data extraction.
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Ultrasound is the most commonly used imaging modality in clinical practice because it is a nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time images. Artificial intelligence (AI)-powered ultrasound is becoming more mature and getting closer to routine clinical applications in recent times because of an increased need for efficient and objective acquisition and evaluation of ultrasound images. ⋯ This has given rise to tremendous opportunities such as providing robust and generalizable AI models for improving image acquisition, real-time assessment of image quality, objective diagnosis and detection of diseases, and optimizing ultrasound clinical workflow. In this report, the authors review current DL approaches and research directions in rapidly advancing ultrasound technology and present their outlook on future directions and trends for DL techniques to further improve diagnosis, reduce health care cost, and optimize ultrasound clinical workflow.
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Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning (ML), has become a reality in clinical practice. Since the end of the last century, several ML algorithms have been introduced for a wide range of common imaging tasks, not only for diagnostic purposes but also for image acquisition and postprocessing. AI is now recognized to be a driving initiative in every aspect of radiology. ⋯ Most of the current AI methods have some internal and external disadvantages that are impeding their ultimate implementation in the clinical arena. As such, AI can be considered a portion of a business trying to be introduced in the health care market. For this reason, this review analyzes the current status of AI, and specifically ML, applied to radiology from the scope of strengths, weaknesses, opportunities, and threats (SWOT) analysis.
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The advent of artificial intelligence (AI) promises to have a transformational impact on quality in medicine, including in radiology. However, experience has shown that quality tools alone are often not sufficient to bring about consistent excellent performance. Specifically, rather than assuming outcome targets are consistently met, in quality control, managers assume that wide variation is likely present unless proven otherwise with objective performance data. ⋯ We consider these elements to be universally applicable, including in the application of AI-based models. We also discuss how the lack of specific elements of a quality control program can hinder widespread quality control efforts. We illustrate the concept using the example of a CT radiation dose optimization and process control program previously developed by one of the authors and provide several examples of how AI-based tools might be used for quality control in radiology.