Tele-commuting has been made increasingly viable with the introduction of new on-line technologies–first with Skype…then with apps like Facebook messenger, G-chat, Apple FaceTime, WhatsApp, Line (primarily for Japan and Thailand), and WeChat (for the world’s Chinese speakers). People can now avail themselves of great tools like Slack, Flock, Yammer, Zoom, Trello, Twitch, and LoopUp…in addition to the old standby, Google Groups / Hangouts.

Such “freemium” social networking services have revolutionized communications; and have thereby drastically augmented our ability to work remotely. As a result, commerce has been completely transformed.

One arena that stands to benefit from this new virtual ecosystem is healthcare. The role of AI in the deployment of healthcare (remotely) will make a profound difference when it comes to functions like tele-assessment, tele-diagnosis, and tele-monitoring.

Take, for example, Glooko, a company that created an app to serve diabetes patients. This SaaS (software as a service) connects people with their healthcare providers remotely. It enables diabetics to upload data from their blood glucose meters (by making use of meter-sync hardware). Meanwhile, the system furnishes healthcare providers with a population-management app, which can be accessed online, providing real-time updates at the touch of a button.

Mobile devices are not the only key innovation. AI is now playing a crucial role in epidemiology. Systems equipped with machine-learning capabilities are incredibly proficient at discerning patterns. They can quickly make predictions based on assaying massive amounts of data–a formidable task that would require an unrealistic amount of man-hours.

Felicitously, “smart” machines are capable of making judgments based on data culled from the IoN (internet of things) located in myriad distant places–data that describes how patients were treated, side effects that occurred, how many were cured, what treatment was most effective, etc. The patterns the system discerns might suggest what the next steps should be. Numbers can be crunched and calculations performed to a remarkable degree without burdening already-overworked medical staff.

AI-driven systems can analyze data on disease outbreaks from doctors on the ground and cross-references what they find with all available medical databases in order to predict when and where an outbreak is happening. Moreover, AI-driven systems are much faster at coming up with accurate diagnoses. IBM launched “Watson for Oncology”–a system that is able to diagnose over a dozen different cancers (which–together–account for more than 80% of the world’s cases). Coupled with tele-health resources, AI is already saving lives worldwide.

By making use of machine learning, scientists can train an AI to perform a task better than a human. This is done by providing the system with thousands of examples–a reminder that epidemiology and diagnostics are logical applications for AI technology.

Major companies like Philips, GE, and Siemens are developing AI for precisely these purposes. Their equipment offers remote patient monitoring to detect problems early. GE Healthcare’s virtual care platform is called “Mural”. The system collects and displays vital signs, displays real-time video, and even renders useful diagnostic images (charts, graphs, etc.)

Meanwhile, the AI-based AIME system was deployed against outbreaks of dengue fever in Malaysia. AIME has a nearly 85% accurate prediction rate–saving thousands of lives and potentially millions of dollars.

Also notable is the FDNA system, which uses facial recognition software to screen patients for over 8,000 diseases and rare genetic disorders with a high degree of accuracy. NYU’s team created an AI capable of scanning thousands of medical documents to pinpoint patients at risk of developing diabetes, heart failure, or stroke. So far it has never been wrong. FDNA uses AI and facial analysis to remotely analyze genomic data and diagnose rare diseases.

And HealthCode AI provides support software (SaaS) for healthcare workers during epidemics. Its communication platform facilitates data exchange and care collaboration; so is a vital tool for medical professionals during the current pandemic.

Such technology combines on-line communication channels with EHR (electronic health records) and CDS (clinical decision support) systems–helping healthcare personnel have all necessary information on their fingertips. The idea is to coordinate collaboration and keep everyone fully informed IN REAL TIME.

Another tech-startup to watch out for is Estonia’s HealthCode AI, which is breaking new ground in AI-facilitated healthcare. The company developed an AI Physician (“Leia”) who is capable of interacting with patients. She also provides physicians with support in daily patient management tasks. Leia does this by using an AI diagnostic platform to pre-evaluate patients, saving doctors tremendous amounts of time.

This is largely about synergy–as machines and humans can complement each other in very productive ways. Tele-health and AI can be developed so that medical decision-making remains in the hands of physicians, while machines can gather information useful to making those decisions. AI software that anticipates–then satisfies–basic needs streamline workflow. This is especially so when it comes to automating tedious and time-consuming tasks. Thus “smart” systems can make healthcare far more efficient and effective while retaining the vital human elements.

86% of healthcare companies have already adopted AI, and plan to spend an average of $54 million on it in 2020. There is ample reason that healthcare organizations are excited by the phenomenal synergies between AI and tele-health.