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announced this week it will partner with , a cancer detection company, to bring its AI-enabled mammography software .

Through the licensing partnership, iCAD will integrate Google鈥檚 AI platform with its suite of AI solutions for breast imaging and potentially improve the company鈥檚 ability to screen for breast cancer at earlier stages.

It鈥檚 the first time Google鈥檚 mammography platform will be tested out in the real world, but it鈥檚 far from the only technology of its kind. In the past few years, artificial intelligence has made its way to the forefront of health care for its promise to solve perhaps the largest problem: finding the disease.

The effects of this kind of infrastructure are far-reaching. Not only could patients access parts of the health care system faster, it could mean less turnover of medical professionals, catching disease earlier, and in general lowering health care spending in a country that, quite frankly, .

In a post-pandemic health care system, those effects cannot be ignored. Though AI in diagnosis has been a popular concept for a while, it wasn鈥檛 until the past couple of years these startups got some serious funding.

Between 2020 and 2021, funding jumped into the billions for the first time in at least a decade. And 2022 saw more than $883 million in funding per 附近上门 data.

The promise of AI

Most people walk around with all sorts of health ailments and never know it until it鈥檚 too late. Part of reason for that is the screening process: Specialists are few and far between, and booking an appointment only to get a screening done isn鈥檛 always the best use of their time or the patient鈥檚.

But what if AI-enabled software could sit at every doctor鈥檚 office, pharmacy and health clinic for a quick breast cancer exam? What if similar technology existed to check for diabetes? Or cavities? What if it was easier to get a doctor鈥檚 appointment because fewer people were going in for checkups? What if doctors could stave off burnout by only seeing patients who need to be treated?

Of course, the technology is still in its early days. The is grappling with how to regulate the data sets these platforms are trained on. And, of course, no health technology will ever make it without buy-in from skeptical doctors worried about black box technology.

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