Nano-pills and graphene sensors: the evolution of patient care?

The management of patient care is seeing a seismic shift. As we skyrocket forwards in the current 4th Industrial Revolution, remarkable advances are redefining healthcare. Hyper-personalized treatment through nanobots, 24/7 mental health support platforms or pre-emptive tools that alert physicians to issues before they become dangerous… this is tech-for-good in action.

As a global futurologist and documentary host, I’ve made it my business for the past two decades to hunt down and uncover the most creative applications of meaningful technology that will sustain and go the distance. 

Rather than these powerful inventions, ideas and innovation taking the human touch out of the equation, I see them as reshaping our approaches to providing medical aid in a way that vests more control than ever back in our hands – both for the ultimate beneficiary (the patient) and the ultimate provider (the clinics, laboratories, healthcare giants and physicians).

Many are reducing the tedious aspects of a healthcare professional’s job and freeing them up to focus on the more critical decision making – which could translate into more lives saved and an enriched level of care for the patient.

In this piece I’d like to help you navigate those applications of tech that I believe will have significant impact on the healthcare industry.

Curating them by the purpose they serve, let’s explore just how these developments will make a dent on the global health economy.

Goal: Preempting illness

 

Medical nanobots anyone?

 

Attempting to help in the prevention of certain medical conditions altogether are medical nanobots. Predicted to be flowing through our bodies by as soon as 2030, these are minuscule devices that can be injected safely into our bloodstreams for precision drug administering and more. The device is tiny – averaging one millionth of a meter (said to be equivalent to or even smaller than a red blood cell).

Nanobots are believed to have the potential to tackle malignant tumors, reduce plaque in veins and address dietary issues, along with a slew of other medical uses.

In the future, I see nanobots as being used to fix cellular damage, replicate to address a genetic issue and even helping eradicate tumors. Until we can prove their accuracy however, we’ll need to exercise some caution.

Digital tattoos and biosensors

 

It’s perhaps a widely recognized fact in healthcare that the earlier a disease or illness is discovered the more effectively – and efficiently – it can be treated. Biosensors themselves are not new, but the current slate of deeply advanced versions are starting to get very close to tackling the issue of early diagnosis.

Already, devices integrating sensitive sensors help in the continual monitoring for type 1 and type 2 diabetes. Whilst recently being trialed in a variety of formats such as digital tattoos (slip-on strips you apply safely on your skin and remove when done) and clothing (for instance, the HexoSkin shirt weaves sensors in and tracks breathing and heart rate), their use case is rapidly expanding.

I’ve been following the healthcare tech company MC10’s work in this space for years. In just May this year, partnering with a global biopharma company, they announced clinical trials using this technology to explore “a range of outcome measures” in patients with multiple sclerosis (MS).

Innovators are working hard to use systems chock full of biosensors to detect cancer early. And they’re not only useful for capturing data for an initial diagnosis, but also for the ongoing monitoring of patients. For instance, monitoring in-skull pressure for those with brain injuries, evaluating the impact of medication being taken and tracking crucial vitals.

We’re also seeing experimentation with different materials. For instance, Osaka University researchers have created a biosensor using the material graphene to detect bacteria associated with stomach cancer.

Graphene is great because it is so versatile and has such a high surface-to-volume ratio that even a tiny amount of molecules will change its conductivity and allow the detection of said molecules. This detection can then be sent remotely to a device used by the patient or medical staff.

 

Advanced artificial intelligence (AI) and the internet of ‘everything’

 

AI is an area of tech that has made perhaps the most headway when it comes to impacting the healthcare industry. Here’s a powerful example – AI tools are able to interpret mammograms a whopping 30 times faster than the norm, and with 99% accuracy. This is drastically reducing unnecessary biopsies (The American Cancer Society report that “a high proportion of mammograms yield false results, leading to 1 in 2 healthy women being told they have cancer”).

Already, computer vision AIs are now able to diagnose many skin conditions at a greater accuracy than a human specialist. Additionally, natural language processing algorithms can detect oncoming psychiatric episodes in patients from tiny markers in speech days before the actual event.

Not only that, health data can now be tracked over a person’s lifetime and tiny patterns in that data can be used to predict health issues years in advance.

AI is also being used to not only develop but to trial drugs at a far faster rate than ever before through real time simulations. For instance, Innoplexus AG based on the outskirts of Frankfurt use an algorithm to analyze pharma companies’ drug pipelines and recently correctly predicted the likelihood of an Alzheimer’s drug trial meeting its goals.

When we combine sophisticated algorithms with the internet of things, we take this up more than a notch. Objects or sensors that ‘talk’ to each other (and subsequently the patient’s smartphone, sending it valuable info) generate swathes of critical data. But this information is meaningless without context. This is where clever AI tools come in – they rapidly analyze the information to give healthcare professionals a fully contextual look into the health patterns of the people under their care.

Notably, IBM Watson Health applies cognitive AI (training software to think like humans) to store vast amounts of medical journals, case studies and more; and then is able to dive into and mine the data to find what we need at breakneck speeds. And PwC report that Google’s DeepMind Health marries machine learning and systems neuroscience to build powerful general-purpose learning algorithms into neural networks that mimic the human brain, to address real-world healthcare issues.

 

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