New technology taking healthcare to a new level

New technology is being adopted and implemented in all areas of the healthcare ecosystem and is changing the way we all receive healthcare. From diagnosis to treatment pathways, the emergence of important new tools is starting to transform healthcare as we know it.

Shara Evans, futurist and expert in artificial intelligence, robots, cybersecurity and jobs of the future, spoke to us about how ingestible scanners and pills with tiny cameras are set to give us a much better picture of what is happening inside of our bodies without having to go through invasive procedures, and why AI will not replace humans for some time to come.

Q: How have you seen new technology in healthcare shift over the years? And from your perspective, what are the implications on the laboratory experience?
 

Shara Evans:

In the last 10 to 15 years, there's been an exponential growth in technology capabilities and this growth just keeps skyrocketing. The combination of artificial intelligence, robots and quantum computing are all going to have a profound effect on the healthcare industry. Our smartphones and other computer gadgets are also playing a really big role in healthcare, along with technologies like augmented and virtual reality, both for consumers and for medical professionals. 

We’re seeing some really exciting new technology come to the market in healthcare at the moment. One company has designed a portable ultrasound scanner. It's really rather small, and can image almost every part of your body. They even have a version that's available for animals too. 

We’re also seeing products like ingestible embedded sensors coming to the market. I'm talking about little tiny chips that can be embedded in medicine that might connect to a patch on your arm or somewhere else on your body. These sensors can communicate with a smartphone and take real-time diagnostics of different aspects of bodily functions.¹ There are also pills that actually have teeny tiny cameras embedded in them. You can literally take the pill and get image diagnostics from inside the body without actually going in for surgery.²

Another huge area that I think is going to be a game changer, and we're really starting to see some advances over the last few years, is smart contact lenses. And with smart contacts, what we're talking about is a combination of ICT, augmented and virtual reality and biotech. Originally it was thought that it would really be mainly in the ICT and augmented reality area that we'd see products come to market first. However, I'm already seeing medical smart contacts that have FDA approval, that can measure intraocular pressure to look at glaucoma, or treat burn wounds in the eyes. There are also companies that have designed smart contacts that release antihistamines or other drugs.³

Another major breakthrough we've seen over the past few years is 3D bio printing. In many cases, part of the ink that's used is comprised of a patient's stem cells, and what that means is that whatever is printed is completely compatible with a patient's biology. So it may be a skin graft, it may be veins, it may be an ear. These things are already possible today, and experiments are currently happening where they're looking to be able to 3D print entire organs, like a heart or a liver.^4,5

Another amazing technology is the shrinking down of everything on a nano scale, including robots. In the healthcare space, this nanobot technology is still in research lab trials, but the goal is that a nanobot can be injected into a person's bloodstream to target specific types of diseases like a particular type of blood cancer. Once it finds the protein affiliated with a particular type of disease, in this case, a particular type of cancer, it can inject a drug that will stop that cancer from growing. This is an exciting example of very, very targeted precision medicine.

Q: Based on your insights, what are the predominant effects of these new technologies on lab productivity and efficiency?

Shara Evans:

With the significant growth of data-driven technologies, there are going to be challenges in integrating AI-based software and big data into workflows. Ultimately, with the use of AI in labs, I think we will, at some stage, see a connected healthcare network that includes the internet of things, all kinds of automation, robotics, sensors, augmented reality and real-time data feeds. Most importantly, we'll have to invest in educating people on how to use these technologies and how to implement the organizational and workflow changes that will be required to support it.

I also very strongly believe that it's still the expert human element of correctly interpreting all of these different lab and cool technology results that will distinguish labs from an automated system. So I think the system integration of lab results in the context of a particular person's complete medical history could be a huge differentiator for clinical labs, assuming that we find a way around all the cybersecurity and privacy challenges that exist.

Q: From a global perspective, are there any major innovative changes in care delivery models you deem will make a significant impact on the future of labs?

Shara Evans:

I think the trio of artificial intelligence, robotics and quantum computing are going to be the most important in the very near term.  Let me give you a specific example. There are now robots that are able to help automate blood analysis systems so that you can cut down on the volume of blood that you need to take for analysis. That is a game changer for very young babies and elderly patients who may be limited in the volume of blood that they can give. There are also a lot of new innovations for scanning and analyzing pathologies that are also being invented, and many new pathology oriented apps for smartphones and other consumer gadgets are becoming available.

This has the potential to take business away from labs and into the hands of a medical professional or even consumers. 

Labs need to start thinking and asking, at the executive level all the way down to the working line staff, questions such as, what can we do with big, expensive instrumentation systems and expert knowledge that can't be done elsewhere? How do we leverage our expertise in different ways? 

I also see new areas where labs can invest already today. One example might be advanced DNA analysis and right now, from what I've seen, a lot of this is done just in very specialized labs, not across the board.

Another thing that can be done today is genome analysis to look for different types of genetic oriented diseases from both a diagnostic perspective and also a preventative measure. Eventually with genetic engineering, we may also be able to do preventative genetic engineering to turn off particular genetic switches.

Q: In your opinion, what can the healthcare industry expect in terms of lab innovation in the near future and five to 10 years down the line?

Shara Evans:

I think that a lot of today's lab instruments, and big data analysis tools in particular, will be completely revamped over the next two to seven years as AI continues its rapid acceleration and is supercharged by quantum computing, which is going from experimental to commercial. 

Another thing that I think will be a big change in labs will be automation, where many of the tasks that are done manually today will likely be done by robots. There will be new jobs in both training robots and AI. So for training AI, there's something called reinforcement learning. For training robots, there's something called imitation learning, where you're actually in a virtual reality environment using telepresence, maybe remotely, to control a robot and teach it how to manipulate things spatially.

We're still going to need human beings to monitor these big data sets that feed AI, and it's going to be super, super important to check the data for accuracy biases.  I've seen examples of smart AI scanning the web for medical articles and then mixing up the facts. Just because a process can scan through massive amounts of data at a super fast rate, doesn't mean it's going to come up with the right information.

Looking 10 years out and beyond, it's quite possible that AI will have progressed to the point where mistakes are less and less likely. At that point we'll need to focus not just on, is the data right, but also on the ethics behind those AI decisions.