There is a big role artificial intelligence can play in helping to shape the future of web-driven consumer healthcare and that future is happening now.

There is a big role artificial intelligence can play in helping to shape the future of web-driven consumer healthcare and that future is happening now.

The ubiquity of artificial intelligence is indisputable. Manifestations of machine learning, deep learning and neural network algorithms are deployed in almost every vertical industry, from equipment asset monitoring to real-time financial services analysis.

Nonetheless, the future of these technologies likely depends on their consumerization in the form of mainstream adoption by the general public. It’s what artificial intelligence can—and is—doing in the personal, as opposed to professional, lives of users which represents its most exciting developments today. This very much holds true for healthcare.

Already, entities such as Uber, Google, and Tesla are manipulating autonomous vehicles in various parts of the country, as passengers rely on these vehicles for their daily transportation needs. In healthcare, sophisticated remote patient monitoring systems can anticipate and prevent undesirable patient outcomes while achieving long term goals. Most desktop computers and a growing number of mobile devices are equipped with artificial intelligence virtual assistants readily predicting future actions to increase user efficiency and productivity.

These developments herald what is effectively the latest frontier for these cognitive capabilities: the consumerization of artificial intelligence and its burgeoning influence on daily life.


Artificial intelligence for healthcare 

The predictive nature of artificial intelligence is rife for a host of use cases in healthcare, particularly those involving chronic diseases. For instance, the multitude of conditions encompassing cardiovascular disease makes it one of the most fatal ailments in the world. It is currently responsible for over 17.3 million deaths each year, a figure projected to exceed 23.6 million in 2030 according to the American Heart Association.

Traditionally, cardiologists leveraged Holter monitors as a means of recording the cardiac activity of patients for 48 hours, before analyzing it via generated reports. A Holter monitor is a battery-operated portable device that measures and tape records the heart’s activity continuously for 24 to 48 hours or longer depending on the type of monitoring used, according to Contemporary real-time data monitoring techniques for remote patient monitoring can instantly relay cardiac activity to physicians as it occurs, superseding the former method in efficiency and productivity.

Artificial intelligence technologies can profoundly impact real-time monitoring, as their predictive capabilities can anticipate cardiac events in time to prevent them. This is extremely important for enabling patients to take the requisite action to mitigate cardiac failure. The focus on such preventative care also translates into cost reductions for healthcare services in general, since costly procedures for adverse outcomes are equally reduced. Moreover, the pairing of artificial intelligence with remote patient monitoring empowers patients to assert greater control over their care management via devices that allow them to actively monitor their particular health conditions.


As individuals use remote patient monitoring applications, artificial intelligence will enable such technologies to learn and adapt based on individual user behavior. This personalization would lead to care suggestions tailored to individuals as opposed to those engendered by generalized algorithms. The effect is analogous to a virtual care team learning and adapting its recommendations based on the underlying data generated by an individual’s personalized remote patient monitoring applications. This scenario will become the future of healthcare: medicine and care management will become personalized to maximize prevention and outcome improvement.

Revolutionizing care management costs

According to The Centers for Disease Control and Prevention, approximately 86% of U.S. healthcare costs are targeted towards chronic disease treatment while roughly 70% of yearly deaths are attributed to chronic diseases. Counteracting these conditions with artificial intelligence and remote patient monitoring measures could result in a situation where, according to London’s Center for Health and Human Performance founder Dr. Jack Kreindler, the healthcare industry can “use these technologies to eliminate all avoidable hospitalization and in the process solve the trillion-dollar problem of chronic disease, which is crippling our economies.”

This vision is shared among many in the healthcare space who are working actively to achieve this goal. The reinforcement of healthcare objectives via wearables technology has resulted in the production of Food and Drug Administration sanctioned and medically verifiable remote performance monitoring devices, which can substantially increase patient outcome effectiveness with the induction of artificial intelligence. For example, deep learning can impact such wearables focused on specific conditions like remote cardiac monitoring at an individual level by indicating how to personalize algorithms according to one’s particular biometric and patient data.


The incorporation of machine learning can assist in the interpretations of the analysis of the unstructured data delivered from these medical-grade wearable devices. The initial analysis is typically provided by mathematical algorithms trained to detect anomalies in this data. Machine learning, combined with artificial intelligence, would then seek to perform an interpretation of such a report, just as a physician would, in order to save physician time. Such capabilities effectively reduce physician time, enabling them to focus on the most critical patients and streamline the care process.

In a mobile world

No doubt, deep learning and machine learning are notable as a tangible demonstration of artificial intelligence ability to improve costly chronic healthcare management and a means of implementation through mobile technology based wearables. This idea is central to the mainstream adoption of artificial intelligence, particularly when one considers the abundance of digital assistants in contemporary smartphones and the obvious mobile implications of autonomous vehicles. By aligning itself with the broadening mobile capacities of contemporary society, artificial intelligence can become as influential, and mainstream, as mobile itself.

Waqaas Al-Siddiq is CEO of  Biotricity