Four Types of AI And Why it Matters for Health Care

Artificial Intelligence (AI) is catalyzing change across industries worldwide. This is especially true for healthcare, as AI ushers in a new era of rapid automation, broad access and a higher quality of care for patients. Physicians and other medical practitioners are reaping the benefits too, as AI machines remove the physical and human constraints that limit speed, complexity and precision of care delivery. How? I’m glad you asked. Here’s how four distinct types of AI are revolutionizing health care:

Cognitive Robotics

Cognitive robotics go beyond motor skills and physical movement, using evolving perceptions to formulate new ideas, plan new actions and learn from the results. Adapting to their environment, cognitive robots can make predictive decisions and in-the-moment adjustments through their exceptional capabilities to embody cognition. In health care, this has a wide-range of applications, from robots that aid surgeons--or conduct complex operations on their own with a greater rate of precision. Cognitive robotics can also be used in healthcare settings for repetitive tasks such as physical therapy or rehabilitation.

Intelligent Automation

Today, the volume of data available to medical providers is increasing every minute. Enter intelligent automation, which is enabling more seamless and automated care delivery and administration by sorting through data to develop more personalized treatments for patients. Intelligent automation has the ability to complement human operations using speed and scale to deliver faster, more accurate results. The result? Digital robots that can interact with patients during check-in for medical appointments, and virtual care providers answering questions based on symptoms and other information supplied by patients. Rather than replacing people, intelligent automation equips healthcare workers to operate more effectively in roles where humans excel.

Machine Learning

Speaking of mining through data, machine learning enables complex math calculations to be applied to larger data sets repeatedly, and with increasing speed. As computers learn to recognize things, ongoing data continually updates machine learning models. This scalable, accurate and accessible data--often collected by wearable devices and sensors--can be used to help medical care providers quickly analyze trends or identify red flags in data, leading to improved diagnosis and treatment at the point of care. Today, machine learning is supplementing radiologist skills by picking up subtle changes in imaging scans, potentially leading to earlier diagnoses of life-threatening diseases. Likewise, doctors are able to address risks quicker, referencing all kinds of patient history information through predictive analytics in order to improve clinical and behavioral outcomes.

Robotic Process Automation

Finally, robotic process automation (RPA) refers to computer systems that are capable of automating activities that previously required human judgment. This automation applies to operational tasks that result in cost reduction, better efficiency and improved analytics. It also results in increased quality, engagement and innovation as healthcare workers can focus on deeper patient interactions rather than mundane activities. Use cases for RPA include claims administration, insurance member management and provider management, improving processes for things like account enrollment, billing management, credentialing and customer service.


So, are humans going to be out of healthcare jobs? Not at all. Rather, AI and humans will work side-by-side to deliver a higher quality, lower cost of care that focused on personalized treatment for patients. Consider AI the new co-worker you’ve always wanted.

Revolutionizing the Future of Health Care Through Machines, Platforms and Crowds

Moore’s Law predicted that computing would dramatically increase in power and decrease in relative cost at an exponential pace. This increase in affordable and powerful computation has resulted in major economical, technological and societal impacts, driving pervasive breakthroughs across all industries--even health care.

Founded on the same relative dynamics of Moore’s Law, Machine, Platform, Crowd: Harnessing Our Digital Future written by Andrew McAfee and Erik Brynjolfsson analyzes the framework shaping the digitally-powered business landscape of today. In the book, the authors describe three shifts which are fundamentally disrupting industries and lives. These shifts include moving from the human mind to machines, from products to platforms and from core businesses to crowds.

As these principles establish a sense of urgency for business models to adapt to new technologies, there are significant applications for machines, platforms and crowds in health care--an industry known for lagging behind in modernization.


The first shift is moving from the human mind to machine as digital technologies continue invading the physical world. This notion suggests that we have entered into an era where machines have mastered cognitive tasks, far surpassing human expectations. With that comes the ability for machines to supercharge businesses through intelligent automation and machine learning that remove human constraints and physical limitations. For healthcare, this means being able to deliver a higher quality of care at a lower cost to a broader audience.

The following are a few applications for the future of machines in health care:

  • AI in Diagnostics: Machine learning provides the ability to test and diagnose a variety of illnesses with improved accuracy (e.g. mammograms, pathology interpretation, etc.)
  • Remote Patient Monitoring: Intelligent automation is enabling remote patient monitoring and personalized treatment via chatbots and other mobile solutions--accessible anywhere, anytime.
  • Nanobots: Robots capable of automating complex actions while being able to manipulate their environments are being used to gather and communicate information about internal organs; augment memory; surgically repair body parts; and deliver drugs to precise locations.
  • Robotic Surgery: High resolution robotic assistance can eliminate limitations like speed, complexity and precision for dangerous operations.
  • 3D Printing: 3D Printing offers a more cost-effective alternative to traditional prosthetics, as well as reconstructive surgery.


Secondly, industries are shifting from products to platforms, using mobile devices to efficiently connect people to services. Platforms provide visibility, amplification and connections. Easily scalable, platforms also reduce waste while increasing consumption--and profit. For example, companies like Uber--the largest taxi company--own no physical cabs and yet foster a marketplace where both clients and providers benefit. Applied to health care, this translates to more convenient options for access and treatment through technology like:

  • Virtual Reality: VR as a platform enables healthcare providers to plan and practice complex operations. It can also facilitate therapy for patients wanting to manage pain.
  • Gig Economy: In support of collaborative consumption, solutions like Iggbo enable healthcare companies to automate the process of procuring, dispatching, tracking, and paying their labor to perform services.
  • Augmented Reality: Putting information into eyesight as fast as possible, AR has practical applications such as helping nurses find veins more readily, or leveraging wearables like AR glasses to view patient data while interacting face-to-face with patients.


The third shift is defined as a movement from the core--centralized institutions--to the crowd, which lowers the cost of interaction while perpetuating greater experimentation and innovation. This is most clearly demonstrated in the difference in how professionals maintain and curate encyclopedias versus how participants on the internet collectively manage contributions to online repositories. Crowdsourcing, for example, is faster and more readily available than traditional data sources. In healthcare, this provides opportunities for patients and clinicians alike to contribute their individual experiences and expertise in discussions in the following ways:

  • Public Crowds: Patients can share their experiences with illnesses or conditions publicly to solicit feedback and advice from others experiencing similar illnesses.
  • Private Affinity Crowds: Affinity crowds could consist of clinical specialists within a specific area convening within a private platform to collaborate and share information.
  • Hybrid Crowds: Affinity crowds may collaborate to create content within blog-type frameworks that is shared with the public, allowing experts to control the information but expose findings to a broader audience.
  • Non-human Crowds: Crowds composed of robots or other AI are able to teach each other, and in turn share that information with other forms of AI, exponentially increasing the number of robots who can do or understand certain tasks.


Within all of these shifts--mind and machine; product and platform; core and crowd--there is no perfect balance. However, the rapidly changing world is shifting towards the latter in each. Applying this framework to the healthcare industry will enable providers and companies alike to improve the accessibility, quality and cost of health care that patients today expect. As more patients assume responsibility for their own health, hospitals, pharmacies, insurance companies and medical providers must chose to quickly adopt disruptive technology or face falling behind.