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Everywhere Care: How analytics is revolutionizing the healthcare industry

Artificial intelligence, blockchain and cloud computing power the journey from “one-size-fits-all” to personalized medicine.

Manjeet SinghBy Manjeet Singh

Healthcare is an industry that touches all of us, given the most basic of human goals: we all want to lead healthy lives. While humans may share the desire to live a long, healthy life and 99.9 percent of the same DNA, there are more than three million tiny differences between your genome and anyone else’s that contribute to differences in your appearance, and, yes, your health.

Yet, until recently, healthcare research focused on one-size-fits-all solutions. Clinical trials aimed to discover therapies for the general population, and solutions were only made available after years of expensive initial research, lab testing and multiple phases of patient testing. But what if all treatments were targeted at your individual genome, from lab testing to ready product, and at a lower cost?

Artificial intelligence is bringing a paradigm shift to healthcare, powered by the increasing availability of data and analytics techniques. Source. ThinkStock

Artificial intelligence is bringing a paradigm shift to healthcare, powered by the increasing availability of data and analytics techniques.
Source. ThinkStock

Artificial intelligence is bringing a paradigm shift to healthcare, powered by the increasing availability of healthcare data and the rapid progress of analytics techniques. The convergence of AI, blockchain and cloud computing technologies will accelerate innovation and revolutionize the healthcare industry via faster research and drug discovery, mobile healthcare and support, use of AR/VR for medical training, and personalized medicines and treatments tailored to an individual’s genomic makeup.

 

AI can be applied to various types of healthcare data (structured and unstructured); popular AI techniques include machine learning for structured data and natural language processing for unstructured data. Figure 1 shows a roadmap from clinical data generation to natural language processing (NLP), to machine learning (ML), to data analysis to clinical decision-making.

Figure 1: Roadmap from clinical data generation to clinical decision-making.

Figure 1: Roadmap from clinical data generation to clinical decision-making.

For example, NVIDIA Corporation aims to tailor treatments to an individual’s genomic makeup using deep learning systems. Others, including a team at the University of Toronto, are building genetic interpretation engines to pinpoint cancer-causing genetic mutations in individual patients. Similarly, researchers at the University of North Carolina’s Lineberger Comprehensive Cancer Center use cognitive computing to identify individually relevant therapeutic options based on one’s genetic profile.

 

This is only the beginning. For example, the biotech company Emulate has raised millions for use of organs-on-chips to replace traditional animal testing and deliver personalized medicine. In the future, these could be your cells on a chip, tested with treatment after treatment until the right one sticks, tailored exactly to your genetic makeup.

But it doesn’t stop at genetically personalized treatments. These techniques can also be applied on personalized diets. Each of us has about 40 trillion microorganisms that occupy our gut, and each microbiome is distinct. Through a simple home kit, Viome applies machine learning to analyze your microbiome, recommending optimal, personalized nutritional recommendations for your gut.

Figure 2: Emulate uses organs-on-chips to accurately test drugs on individual human organs. Source: Emulate

Figure 2: Emulate uses organs-on-chips to accurately test drugs on individual human organs.
Source: Emulate

Bowhead Health tackles yet another approach to personalized medicine. With either saliva or a blood-prick test, Bowhead’s small home device reads the biometric data in real time and transmits the reading to doctors. As soon as key deficiencies are identified, your in-home Bowhead device dispenses a customized, vitamin-based pill.

Delocalized (everywhere) Care

George Halvorson, former Kaiser Permanente’s chairman and chief executive, foresees plummeting healthcare costs and care migrating farther from hospitals and doctors’ offices and into any and every setting via the Internet. Here are the technologies that are making it possible:

mHealth or mobile health has already grown beyond a $23 billion market, and by some estimates will surpass $102 billion by 2022. AI-powered medical chatbots are flooding the market. Diagnostic apps can identify anything from a rash to diabetic retinopathy. And with the advent of global connectivity, mHealth platforms enable real-time health data collection, transmission and remote diagnosis by medical professionals. New diagnostics and screening apps are also beginning to empower the next generation of patient-doctors.

In addition to phone apps and add-ons that test for fertility or autism, the now-FDA-approved Clarius L7 Linear Array Ultrasound Scanner can connect directly to iOS and Android devices and perform wireless ultrasounds at a moment’s notice. With mHealth platforms like ClickMedix, which connects remotely located patients to medical providers through real-time health data collection and transmission, providing needed treatment is possible through drone delivery and robotic surgery.

AR/VR (augmented reality/virtual reality) will revolutionize medical training, making it immersive and ubiquitously accessible. It’s no wonder the healthcare industry suffers from a shortage of doctors. Medical training is not only expensive, but its conventional methods also severely limit scalability. With virtual and augmented reality, however, gone are the days of peering over a surgeon’s shoulder to learn from another’s experience. Companies such as Echopixel and 3D4Medical are achieving this delocalization and hands-on training with remarkable style, translating 2D scans and anatomy into live AR and VR patients.

AI-aided IoMT (Internet of Medical Things) may be one of the most exciting frontiers in healthcare. While wearables have long been able to track and transmit our steps, heart rate and various other health factors, smart nanobots and ingestible sensors will soon be able to monitor countless health parameters and even help diagnose disease. But it doesn’t stop there. As nanosensor and nanonetworking capabilities develop, these tiny bots may soon communicate with each other, enabling the targeted delivery of drugs and autonomous corrective action.

Some companies, however, are working on high-precision sensors that need not enter the body. Apple, for example, is reportedly building sensors that can noninvasively monitor blood sugar levels in real-time for diabetic treatment. Digital pills such as Abilify will now be able to communicate medication data to a user-controlled app, to which doctors may be granted access for remote monitoring.

Intelligent Prevention

Take a minute to imagine this unprecedented convergence in the near future. Nanobot sensors flowing through your bloodstream monitor different health parameters, measuring nutrient levels and keeping an eye on your cholesterol. As data flows in, these connected sensors transmit your health data in real time to a remote AI-powered supercomputer geared with all your genomics, microbiome and medical history data with access secured via blockchain.

As abnormalities are detected, this AI-driven doctor sifts through tomes of data to identify an optimal, personalized treatment based on your genetic profile and real-time health data. Once vetted and approved, a prescription arrives at the dashboard of your in-home medical 3D printer. With customized dosage, your 3D printer separates the drug’s active ingredients with microbarriers and embeds a printed sensor to monitor variations in drug release and effectiveness. Feedback is instantaneously communicated through IoMT, and AI again improves its personalized medicine for future treatment.

You might think that AI medical powerhouses and autonomous sensors leave human doctors out of work. Instead, many digital healthcare startups are in fact redefining and elevating the role of doctors by liberating them from many of the tedious necessities that so often constrain their ability to engage with patients.

As medical AI enterprises such as Microsoft’s Healthcare NExT and IBM Watson Health bring incredible power to diagnostics, drug discovery and genetic therapy development, doctors may be freed to take on consultative roles – educating patients, performing many more remote surgeries with the help of robotics, and aiding in preventive care.

Summing Up

Several themes emerge at the convergence of healthcare and technology. The explosion of data, incredible advances in computational biology, genomics and medical imaging have created vast amounts of data well beyond the ability of humans to comprehend. As transformative technologies such as CRISPR-Cas9 unlock our genetic potential, quantum computing massively ups the speed of AI-powered drug discovery, 3D printing places the power of preventive medicine in the hands of consumers, and next-generation implants enhance our minds, we are truly living in an era when anything is possible.

Big tech companies like Microsoft, Google and IBM are partnering with healthcare industry’s most pioneering players, allowing doctors and patients to be able to use AI and the cloud to unlock biological insight and break data from silos for a truly personal understanding of human health and, in turn, enable better access to care, lower costs and improved outcomes.

Manjeet Singh is the senior product manager, ITSM Intelligence at ServiceNow in Santa Clara, Calif. Singh has 16 years of experience building tech products and solutions for enterprise and consumer markets. He holds a bachelor’s degree in computer engineering and an MBA from Santa Clara University.

  1. Genome News Network
  2. Microsoft blog, 2018, “Microsoft’s focus on transforming healthcare.”
  3. Fei Jiang, 2017, “Artificial intelligence in healthcare: past, present and future.”
  4. Matt Collier, 2017, “Artificial Intelligence: Healthcare’s New Nervous System.”
  5. Graham, J., 2016, “Artificial Intelligence, Machine Learning, and the FDA.”
  6. Peter Diamandis, tech blog, 2018, “Revolution in healthcare.”
  7. Murdoch, T.B., Detsky, A.S., 2013, “The inevitable application of big data to health care,” JAMA, Vol. 309, No. 13, pp. 1351-1352

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