But with so many demands placed on radiologists, hiring more will not suffice. Integrating technological enhancements like artificial intelligence<\/a> in diagnostic radiology is necessary. In this article, you\u2019ll learn more about the potential of AI in medical imaging and its possible applications.<\/p>\n Artificial Intelligence & Machine Learning Services<\/p>\n AI-based radiology software uses machine learning algorithms, big data, and computer vision for medical imaging to view and interpret MRIs, CT scans, CAT scans, and other images, augmenting a radiologist\u2019s performance or sometimes functioning as a standalone tool.<\/p>\n The fundamental steps of classic machine learning and computer vision techniques in medical imaging<\/a> include:<\/p>\n For example, say we want to recognize a type of plant. First, we determine that an image is of a plant (image acquisition). Then we decide that one part is a stem and the other part is a leaf (segmentation). After that, we determine that the length of the stem and the color of the leaf are the most important features for identification (feature selection and extraction). Finally, after analyzing these two features, we decide that the plant is an eggplant, not a tomato (classification).<\/p>\n Applying artificial intelligence to image processing in the medical field has the immense potential to simplify and accelerate the entire radiological workflow while significantly reducing the rate of missed diagnoses and improving health outcomes. Below are a few characteristics of AI in radiology and practical values it already brings or might bring in the future. AI-driven medical imaging solutions rely heavily on large volumes of medical data to train their machine learning algorithms to detect abnormal scans. When interpreting scans, AI-based solutions not only compare the scans with cases they\u2019ve learned but also consider additional medical information, including previous scans of the same patient. This allows an AI solution to make diagnoses based on context, boosting precision manifold. Additionally, data obtained from previous scans can be uploaded into an electronic health record system for future reference.<\/p>\n AI-powered computer-aided diagnosis technologies can find abnormalities that the most experienced and detail-oriented radiologist is unable to catch. By giving radiologists consistent and reliable data, artificial intelligence in medical imaging can minimize subjectivity and variations in interpretations between radiologists. In this way, it can improve the reproducibility of radiological results.<\/p>\n Using the power of computer vision in healthcare applications, AI systems can interpret images in context, making them similar to human radiologists. Since not all abnormalities are necessarily representative of a disease, ML algorithms in AI-based CAD solutions are trained on a case-by-case basis to see features invisible to the human eye. This makes them a decent alternative to conventional CAD systems, which just detect the absence or presence of visible features. Read also: Computer Vision Application Examples<\/a><\/p>\n <\/div> \n <\/div><\/p>\n The characteristics mentioned above suggest that artificial intelligence can automate many routine tasks (such as detecting, characterizing, and quantifying abnormalities) as well as more complex tasks (such as interpreting findings within the clinical context).<\/p>\n The rising demand for processing large and complex datasets makes radiologists turn to AI-based technologies. As a result, the global AI in medical imaging market size is forecasted to expand at a compound annual growth rate (CAGR) of 34,8% by 2030.<\/p>\n Source: Grand View Research<\/a><\/em><\/p>\n Though AI-based medical image processing is only in its infancy, it has already brought certain practical improvements to the medical imaging field. In an article published in European Radiology Experimental<\/a>, Filippo Pesapane, Marina Codari, and Francesco Sardanelli point out that AI in medical imaging is one of the most promising areas of health innovation.<\/p>\n AI holds tremendous opportunities for improvement of radiology and patient treatment.<\/p>\nThe potential of AI in medical imaging<\/h2>\n
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<\/div>\n\t\t\tAbility to process large volumes of data<\/h3>\n
Increased sensitivity to hidden abnormalities<\/h3>\n
Context-based interpretation<\/h3>\n
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<\/p>\nWhat practical improvements does AI bring?<\/h2>\n
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