Artificial Intelligence [AI] also called as machine intelligence is all set to take the world by storm and the field of radiology is no exception. AI was in the spotlight in the recently concluded conference of Radiological Society of North America [RSNA] held in Chicago, with a number of keynotes and scientific sessions dedicated solely to AI and teleradiology. The number of exhibitors at RSNA conference tagged as machine learning companies more than doubled compared to last year. If this gives an inkling of things to come, AI is here to stay.
A few years ago AI was seen as a threat by the radiology community wherein radiologists perceived AI as a threat meant to replace them, however; the mood seems to be changing from fear, skepticism to optimism and hope. If utilized judiciously, AI can prove as a useful ally to the radiologists and also greatly improve the quality of healthcare delivery. Radiology as a field is largely digital in nature hence specialties such as teleradiology and Artificial Intelligence are bound to make a huge impact.
Shortage of qualified radiologists means the existing ones are overburdened with a huge amount of workload and emergency cases often get buried under the pile of all images. AI can quickly scan and screen large volumes of images and bring the cases requiring immediate attention, such as stroke or trauma at the top of the pile, helping radiologists make emergency decisions – potentially saving lives.
Screening tools by AI can also bring cases requiring more intense analysis at the top of the list so that radiologists are able to interpret complex cases earlier with fresh eyes rather than later on with exhausted eyes and mind. In future simple cases might be offloaded to AI tools in order to divert the focus of radiologists to cases requiring more attention.
Even though the current focus is largely on CT and MRI technology, other modalities such as digital x-ray, echocardiography, mammography, and ultrasonography are also being explored at a big scale. AI can help improve the quality of images by enhancing image resolution by making a smaller amount of data comparable to larger data sets, as well as lower the radiation dose. It will be much easier to analyze more complex scans with a large number of slices using machine learning tools. AI can help radiologists streamline the workflow right from the stage of ordering to the final reporting.
Every technological change is meant to improve the efficiency and outcome of the process, and use of AI in radiology is no different. Digital imaging, CAD technology, and Teleradiology were all initially met with hesitancy and doubt, but are a norm at present. Artificial Intelligence will take some time to become a part of mainstream radiology but it makes sense to be more accepting and embrace the technological advancements which are ultimately aimed to make us more efficient.
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