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The AI sector is growing at breakneck speed.
Artificial intelligence (AI) for medical imaging is moving beyond the hype that blurred the rise of the field and toward a $2 billion global market valuation, according to a new report.
The sector is slated to reach that dollar amount by 2023, banking on automated identification, measurement and decision support tools for medical diagnostics, found Signify Research, a United Kingdom-based health-tech intelligence group. The authors went as far to say that AI — which mostly comprises machine learning at this time — will “transform” the industry, boosting productivity, accuracy, precision medicine and clinical outcomes.
One reason for the enthusiasm behind AI’s ascent in this space is the chronic radiologist shortage facing many countries, coinciding with an influx of medical images. In the past, clinicians considered AI a threat in this area, but the reality of the industry’s work burden has changed that outlook, especially as blue-chip companies have invested in the technology, according to the report.
“Up to now, the market has mainly been driven by the many startups and specialist companies who are applying machine learning to medical imaging, but the major medical imaging vendors are now ramping up their AI activities,” said Simon Harris, an analyst for Signify Research.
Harris pointed to Tencent and Alibaba, two Chinese companies, as recent noteworthy entrants to the market. But major U.S. tech organizations such as Google — you might have heard of its work surrounding melanoma and diabetic retinopathy — have also been developing algorithms to improve diagnoses.
So what else is driving AI’s power for medical imaging? Development has picked up thanks to deep learning, affordable cloud computing and storage, and resultant software programs are maturing. Equally important, evidence that AI tools deliver positive clinical and financial outcomes is mounting.
“Over the coming years, the combined R&D firepower of the expanding ecosystem will knock down the remaining barriers, and radiologists will have a rapidly expanding array of AI-powered workflow and diagnostic tools at their disposal,” Harris said.
But make no mistake: The road ahead won’t be easy. Regulators continue to pose challenges, the industry needs greater clinical validation to foster buy-in, and vendors must better collaborate with information-technology teams to integrate AI imaging tools into existing workflows.
And getting to market is only part of the equation.
“Healthcare providers are reluctant to purchase AI tools from multiple companies due to vendor-specific integration challenges and the administration overhead,” the report noted. As such, developers must strike distribution deals with leading medical imaging companies and ultimately strive for something that has eluded the electronic health records industry: vendor-neutral platforms.
At the same time, U.S. medical regulators are crafting ironclad policies for the use of AI in medical imaging. They could cover everything from data governance and performance metrics to imaging rules and interfaces. How, if at all, this might help or hinder the development of the sector is unclear.
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