Radiologists at RSNA 2020 stand to benefit from evolving AI ecosystems

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Radiologists at RSNA 2020 stand to benefit from evolving AI ecosystems

November 24, 2020
Artificial Intelligence Business Affairs X-Ray
Shane Walker
By Shane Walker

Radiologists attending the RSNA 2020 conference starting Sunday can expect to see a range of validated AI solutions—whether embedded, on premises, or in the cloud—being offered by companies eager to promote the technology, which can reshape medical imaging and transform how radiologists work.

Among companies with AI offerings that will have a presence at RSNA 2020 are Intel, with the Intel AI Builders ecosystem and OpenVINO developer’s toolkit; GE Healthcare, with the Edison Open AI Platform and Edison Developer Program; Siemens Healthineers, with the AI-Rad Companion product line and Digital Marketplace; Samsung Medison, with the BiometryAssist and LaborAssist products; Philips Healthcare, with the HealthSuite digital platform; Agfa HealthCare, with the Enterprise Imaging solution; and Nvidia, with Inception accelerator program and the Clara Imaging platform.

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Radiologists, once wary that AI might altogether supplant their role in hospitals and treatment centers, are increasingly warming to AI because of the clinical and operational benefits possible with the proper use and implementation of the technology. Convening from Nov. 30 to Dec. 2 for the annual meeting of the Radiological Society of North America, an all-virtual event this year because of COVID-19, radiologists will see AI showcased in imaging demos, education sessions, and product exhibits. The international society of over 54,000 global members offers extensive resources on AI, and it issues an AI challenge each year to explore the ways that AI can benefit radiology and improve patient care.

A significant primary benefit in using AI is speed and capacity: Even the most skillful radiologists cannot come close to matching the sheer volume of data that can be analyzed by AI algorithms. Moreover, AI can find complex patterns in images, such as molecular markers in tumors not discernible to the human eye. AI-driven algorithms can also help improve diagnostic workflows by reducing time spent on routine or manual operations involving patient setup, screening, measurement, segmentation, and formatting.

But while there is enthusiasm among radiologists to incorporate AI into their practice, the knowledge to do so is often lacking, according to a study published in October in the journal Academic Radiology. The study found that almost 40% of the radiographers and radiologists participating in the survey were not familiar with AI, and that there appeared to be a mismatch between awareness of AI’s potential and expectations about its role. Given this discrepancy, it seems appropriate to provide some context for AI’s evolving acceptance in radiology—particularly with RSNA approaching.

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