Not all artificial intelligence is born equal. There is the frightening and extremely intelligent artificial intelligence made famous by popular culture like the Terminator movies. Then there is the extraordinarily complex AI that is slowly but surely making its way to things like autonomous cars and cloud services such as Siri and Alexa – and eventually, to medical applications.
However, artificial intelligence does not need to be frightening, nor does it need to be overly complex. On the contrary, specialized and less computing-intensive artificial intelligence can bring tremendous power to medical applications today – without being frightening, expensive, or difficult to implement.
It is time to say hello to AI inference.
A different type of smart
Medical equipment manufacturers and other innovators use AI inference in exciting new ways that make healthcare smarter, safer, and better.
Recently, Innodisk worked together with two industry partners in delivering inference AI platforms to healthcare applications. The service that the platforms provide is simple: they spot people and detect whether or not they wear face masks.
What happens then is entirely up to the client. In the case of strict mask-wearing enforcement, the system may alert nearby security staff. If wearing a mask is simply a suggestion, a display may encourage individuals not wearing masks to buy one using a nearby vending machine. The system can be completely passive as well and just supply data on face mask-wearing frequency to stakeholders.
While AI inference applications like this are brilliant at what they do – usually much faster and more exact than humans – their expertise is highly limited. Ask an inference application trained to detect face masks to do a different task and they will be completely lost. Compared to the sometimes-frightening artificial intelligence you see in movies, AI inference is a different and very specialized type of artificial intelligence.
However, mask-wearing detection is just one of the countless imaginable applications for inference in the healthcare industry. For instance, healthcare service providers use AI inference for things like gesture control of computer interfaces in operating rooms, fall detection in assisted living facilities, not to mention for general security purposes in medical facilities.
Healthcare providers also use inference for putting artificial intelligence to use in diagnosis. For instance, a well-trained AI model can be used for discovering tumors in medical imaging. Such artificial intelligence is not only faster and cheaper than exclusively relying on human intelligence but sometimes it also detects anomalies that humans fail to discover.
Low cost and easy to deploy
Since the AI models used in inference are trained off-site and on huge datasets, medical devices get to avoid all the arduous work and are simply responsible for putting these specialized AI models to use with inference. Consequently, a purpose-designed AI accelerator card is usually all the extra hardware required for computers in medical devices to handle all these exciting new abilities.
Modern AI accelerator cards are compact, affordable, and support a variety of form factors and interfaces, making them extremely easy to integrate into almost any system. Better yet, leading AI companies already supply all the necessary tools to get started, including pre-trained AI models that can be put to use in AI applications in an instant.
Putting security first
Data security must always take a center stage in artificial intelligence applications – not least in sensitive applications such as those in the healthcare industry. Fortunately, with inference, edge AI devices infer conclusions about the data they process independently – without sending anything to the cloud. In the mask detection system that we previously mentioned, for example, the system does not upload photos of each individual to the cloud for processing. On the contrary, it immediately concludes whether or not an individual wears a mask and then uses this information according to its protocol. For instance, it may add another digit to “total mask-wearing patients” and then be done with it. Therefore, the device sends no personal data or photos to the cloud nor does it store any photos or personally revealing data on its internal storage.
Mask detection systems trialed in the Paris metro system earlier this year work very much that way – ensuring GDPR compliance while still bringing innovative AI technology to life.
That is not to say that inference cannot be used in ways that violate patient privacy or facility security protocols. Just like any other IT infrastructure, operators must ensure that AI devices are deployed and used in ways that follow existing security standards and regulations. However, it should be noted that AI inference applications are not inherently a privacy or data security risk.
The natural next step for medical devices
The benefits of AI inference that we have brought up in this article demonstrate why inference is such a no-brainer for medical devices. AI inference is a benevolent and extremely specialized type of artificial intelligence that unlocks considerable benefits throughout healthcare facilities and medical workflows. With the affordability of modern AI accelerator modules and the ready access to a wealth of AI resources, deploying AI inference to enhance even the simplest applications finally makes sense.
Are you well-positioned to benefit from the future of AI inference in healthcare?