AI in Radiology – Many Advances, More Software Protections Needed
09/01/2020 John Poulson
It’s been widely discussed over the past decade as to how artificial intelligence (AI) promises to transform virtually every segment of our economy by bringing human intelligence into computing and allowing machines to learn from experience and make human-like decisions. AI use cases have shown how this technology can help businesses automate routine tasks, better understand their customers by analyzing their behavior, reduce operation costs, and personalize their experience across a wide variety of industries, from finance and banking to retail/eTail, energy and utilities.
So, it wasn’t such a surprise to see that AI and deep learning technologies were trending topics at the recent Radiological Society of North America (RSNA) annual meeting in Chicago, IL USA. What was surprising, however, was how fast AI research in radiological imaging applications was advancing. According to AuntMinnie.com, a community Internet site for radiologists and related professionals in the medical imaging industry, AI research is largely focused on augmenting radiologists diagnosis readiness and improving the practice of radiology and, consequently, patient care. Research has also increased on applying AI with radiomics to assess risk, as well as monitor and predict response to treatment.
Here are just a few headlines that came out of news coverage of the meeting:
And there was much more research reported at the meeting. According to RSNA leaders, “AI is central to the future of radiology. But that doesn’t mean it will be the future radiologist. Instead of seeing the technology as a threat, AI should be embraced as another tool that radiologists can leverage to do their job better — in a multitude of ways.”
At the 2019 meeting, RSNA expanded its AI Showcase to more than 120 companies. The showcase included a Deep Learning Classroom and RSNA exhibit that demonstrated how AI and other decision-support tools can be incorporated and be effective in the radiology workflow.
Like other industries undergoing digital transformations, much of the AI advances in radiology are software and data driven. For example, Siemens demonstrated its AI-Rad Companion, described as a platform for the development of intelligent software assistants capable of identifying organs and changes in tissue that may be early signs of disease. And, like all software-driven applications, strong code protection and secure licensing mechanisms must be designed into the systems to protect them from IP theft, counterfeiting, and malicious tampering.
Take the case of Agfa HealthCare, a leading provider of computed radiography and diagnostic imaging solutions. They employ Wibu-Systems CodeMeter software protection, licensing, and security platform to protect their proprietary software assets and IP from illegal and fraudulent use. They also use CodeMeter to implement time-based licensing that allows healthcare providers to use their computed radiography package in a pay-per-use scenario, which opens opportunities for small laboratories, orthopedic doctors, and healthcare facilities that could not otherwise afford the upfront investment in state-of-the-art equipment.
These are steps all healthcare system developers should consider. If you would like to learn more about CodeMeter and its applications in the healthcare industry, you can read the full Agfa case study here.
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John went to work back in 1987 for what arguably might be the first company in the world to offer a way of protecting software with hardware. This company developed a "back-plane" device to protect a proprietary operating system for a Data General computer. He has since worked for several software security / licensing companies and beginning in 1999, with Wibu-Systems. He has seen the technology move from simple laser holes burned into 5-1/4" floppy disks to the innovative, sophisticated, encryption based smart card technology, first introduced to the world in the CodeMeter platform.