The dust is now beginning to settle on the idea of AI in imaging—an idea that once took the industry by storm. The questions being asked by radiologists have now changed from “will it replace me?” to “how can it help me?”—and rightly so. AI continues to make significant progress in the field of diagnostic imaging, as can be gauged using the recently concluded Radiological Society of North America’s (RSNA) Annual Meeting in Chicago as a barometer.
Related article: What You Missed at RSNA 2018
Last year, there were 49 exhibitors at RSNA tagged as machine learning companies, and 22 of those were first time exhibitors. This year, the same number more than doubled to 104, 25 of which were first-time exhibitors. More importantly, the incumbents of medical imaging equipment have also made notable AI efforts, with each one launching new or enhanced AI capabilities. Clearly, AI was one of the key themes at RSNA.
AI for image analysis
Based on recent analysis, Frost & Sullivan notes that of the 114 startups active in the AI for medical imaging space, a significant majority target the image analysis aspect of medical imaging. Identifying and analyzing specific features in an image form the crux of a radiologists’ job, and since they base their findings on this analysis, it forms the most important clinical step in the imaging workflow. The startup disruption rampant in overall healthcare has focused on image analysis in the case of medical imaging AI.
While computed tomography and magnetic resonance imaging are the top modalities of focus, these are followed closely by digital x-ray, mammography (including 3D tomosynthesis), fundus imaging (of the eye), ultrasound, and echocardiography. While most imaging equipment vendors have not touched upon this area, Siemens Healthineers debuted the AI-Rad Companion Chest CT at RSNA this year, leveraging datasets that add up to 325 million curated and annotated images available for training AI algorithms. The intelligent software assistant can take vendor-agnostic CT images and highlight various thoracic structures, as well as mark any potential abnormalities and include their measurements in structured reports—targeting the heart, aorta, lungs, and coronary arteries. GE Healthcare, on the other hand, launched the Edison platform that brings together all AI algorithms from its existing partnerships.
AI for cognitive workflow applications
Beyond image analysis, there are several other steps in the imaging workflow that can also benefit from AI. Right from the ordering stage, all the way down to reporting, AI solutions either exist or can be developed.
Naturally, image analysis is the most advanced application, followed by assignments—AI applications for triaging, worklist assignment, and workflow orchestration are available from Aidoc, Zebra Medical Vision, vRad, and GE Healthcare, for example.
The next most advanced area is of decision support for helping decide the next steps in the clinical pathway—representative examples include Imaging Related Clinical Context from GE healthcare, IntelliSpace Oncology by Philips, and PowerScribe 360 Clinical Guidance from Nuance.
Without going into the details of all other steps, the two workflow steps that could still benefit from the attention of AI solution developers are the ordering and scheduling steps. Companies providing decision support to physicians are best suited to take advantage of such solutions for the ordering stage, while scheduling solutions would be useful for optimizing utilization via scheduling improvements around cancellations and no-shows, for example. There is ample opportunity for growth in this area, using AI to make the workflow more efficient and support the radiologists in ways not previously possible.
Intelligent medical imaging machines
Equipment manufacturers are gearing up to make their imaging machines intelligent by infusing AI in their scanners, or at the edge. Canon, for example, debuted the Advanced Intelligent Clear-IQ Engine (AICE) image reconstruction solution that uses AI to deliver high-quality images from lower radiation-dose, low-quality images. Siemens Healthineers already has the FAST 3D camera, which helps with patient isocentering, eliminating the need for a higher dose, improving image quality, and preventing repeat scans which may not be reimbursed.