Journal of the American College of Radiology : JACR
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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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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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Adversarial networks were developed to complete powerful image-processing tasks on the basis of example images provided to train the networks. These networks are relatively new in the field of deep learning and have proved to have unique strengths that can potentially benefit radiology. Specifically, adversarial networks have the potential to decrease radiation exposure to patients through minimizing repeat imaging due to artifact, decreasing acquisition time, and generating higher quality images from low-dose or no-dose studies. The authors provide an overview of a specific type of adversarial network called a "generalized adversarial network" and review its uses in current medical imaging research.
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Our understanding of human health may be significantly enhanced in the near future because of the unprecedented volume of digitized health care data and the availability of artificial intelligence to mine these data for correlations that could drive new research hypotheses and improved patient care. Observational studies and randomized trials are traditional methods to generate and test hypotheses. ⋯ In 2018, the National Institutes of Health unveiled its Strategic Plan for Data Science, which includes a far-reaching plan for the use of big data to stimulate new research discoveries. Both researchers and physicians will need to learn and apply new skills in understanding the use of artificial intelligence and other tools, as well as in the direct application of data collection and mining in their own practices and patients.
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Rapid technological advancements in artificial intelligence (AI) methods have fueled explosive growth in decision tools being marketed by a rapidly growing number of companies. AI developments are being driven largely by computer scientists, informaticians, engineers, and businesspeople, with much less direct participation by radiologists. Participation by radiologists in AI is largely restricted to educational efforts to familiarize them with the tools and promising results, but techniques to help them decide which AI tools should be used in their practices and to how to quantify their value are not being addressed. This article focuses on the role of radiologists in imaging AI and suggests specific ways they can be engaged by (1) considering the clinical need for AI tools in specific clinical use cases, (2) undertaking formal evaluation of AI tools they are considering adopting in their practices, and (3) maintaining their expertise and guarding against the pitfalls of overreliance on technology.