The ever evolving technology powered by artificial intelligence is transforming many industries. The healthcare industry is no exception. Artificial Intelligence (AI) and machine learning technologies are increasingly being depended upon to keep up with the influx of constant new information about health conditions, treatments and medical technology. Today, machine learning algorithms and predictive analytics are being used to reduce drug discovery time, provide virtual assistance to patients and diagnose ailments by processing medical images.

From the past to the present

The Information age has brought with it an influx of technology that aims to make healthcare cheaper. Opposed to earlier where manual labor and doctors were heavily relied upon, artificial intelligence in the present can help scan thousands of images and identify patterns at a fraction of time and machine learning can help improve sensitivity and accuracy over time. Such developments make many procedures in healthcare much cheaper than what it has been in the past.

For example, AI can bring down the cost of a cancer screening test, by reducing the time to perform the operation and by bringing down the doctor’s fee, since highly skilled endoscopists may no longer be required to perform screening tests. Medical VR (virtual reality therapy) has been evidenced to stop the brain from processing pain and reduce pain in hospitalized patients. This, in turns, shortens the length of the patient’s stay in the hospital, which, also lowers the costs of care

Key Applications of AI in Healthcare

Virtual Reality

Although VR indeed set sails to enhance the demanding gamer’s experience, it has also made significant improvements to the lives of people with autism, lazy eye, chronic pain, and other health conditions. Startups like Floreo use virtual reality to help make the delivery of therapy simplified so parents can support their offspring from home. Their product uses mobile VR to instigate social interactions with autistic kids by spurring virtual characters in a scene. It can also be used in a manner that influences the brain to reduce chronic pain. A faster recovery time can be clocked using innovative technologies. Mindmaze is a Swiss app that allows patients to practice how to move their fingers or lift their arms in a fun fashion with the help of VR. Although patients do not carry out the actual movement, their engagement, motivation, and attention is notably improved with audio-visual feedback, which could speed the recovery of traumatized nervous systems.

Computer Vision and Robotics

Medical imaging is the biggest and most established area of computer vision and is used by computer-aided diagnostics for personalized therapy planning, care assistance and for better decision-making.

Robotic surgery has been making waves in the industry and is being hailed for being ‘minimally intrusive’, thereby allowing the patients to heal faster from smaller incisions. They also analyze data to guide the surgeon. One popular example is the da Vinci Surgical System features a magnified 3D high-definition vision system and tiny wristed instruments that bend and rotate far greater than the human hand. As a result, da Vinci enables the surgeon to operate with enhanced vision, precision and control.

Among other robots, the HeartLander is a miniature mobile robot that can enter the chest through an incision below the sternum. It reduces the damage required to access the heart and allows the use of a single device for performing stable and localized sensing, mapping, and treatment over the entire surface of the heart.

Virtual Assistants

Virtual nurses are high on demand as they offer many benefits including round the clock availability and quick answers. They offer regular communication between the patients and the care providers. Care Angel’s voice powered virtual nurse assistant provides wellness checks through AI. Another digital nurse is Molly, created by the startup Sensely, which monitors a patient’s condition and follows up with treatments, between doctor visits.

Administrative Automation

AI can help in compiling, managing and analyzing medical records and other data. Automated Administrative tasks can thus save money and time. Robots collect, store, re-format, and trace data to provide faster, more consistent access. Mundane tasks analyzing tests, X-Rays, CT scans can me made faster and more accurate. The data collected and stored can accessed consistently. Technology such as voice-to-text transcriptions could help order tests, prescribe medications and write chart notes. IBM’s cloud based intelligence Watson, mines big data and helps physicians provide a personalized and more efficient treatment experience. It is also among the pioneers of the field.

Doubts still remain

Even though there have been breakthroughs in the healthcare industry with regards to the applications of artificial intelligence, people still harbor fears of mismanaged care due to a mechanical error. The lack of human insight and data privacy issues are other concerns that the industry has to deal with. While technology can support the highly trained medical professionals, the chances of it taking over the industry completely remains very low.

The future

In a few years, the market for AI-powered healthcare technologies will exceed 6 billion dollars. A demand for electronic, data driven and virtual-based care is the driving force, especially because they offer more convenient, accessible and affordable care. Patients look forward to gaining greater insight into their own health and finding a more appropriate level of care for their needs.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.


Everybody who owns a smart phone has already experienced artificial intelligence in their lives. Technology strives to make everything faster and better.

Security becomes an integral part of such a fast-paced life, where humans are distracted by the macro and tend to overlook the micro things around them. According to the U.S Department of Justice, on an average, almost 4 million burglaries take place every year. Artificial Intelligence powered Home security systems rise to the occasion, offering a better and cheaper solution than what was previously offered in the industry.

Things of the Past

Traditional home security systems were laborious and manual in nature. It required  monitoring by people, taking shifts to keep guard 24 hours a day, thereby making it expensive. Even in the owner’s absence, the guards called up the emergency services if someone broke in.

A few years after the traditional systems were deemed insecure, home surveillance systems used simple sensors and alarms to detect intruders. Such systems did not require constant human supervision. If something went wrong, an alarm would go off and the security professionals would be contacted. But in most cases such alarms were false, which resulted in a major waste of time, money and resources.

The Smart Solution

Today, incorporating artificial intelligence into security systems gives it the ability to recognize the difference between an intruder and visitor. Through the advances of technology such as computer vision and deep learning, security systems have the potential to track the visitors and guests. Conventional security systems on the other hand would have sounded an alarm when they detected movement.

Police stations usually report that most of the alarms turn out to be false. Giving the owner the power to disable the alarm when there is no threat present saves time and money but will bypass that if the owner doesn’t respond soon enough, keeping one out of harm’s way.

Lighthouse is an example of a leading smart home camera, enabled with advanced computer vision and AI technology. The brand claims that it creates a 3D model of the room to understand what it sees, using technology from self-driving cars. Modern security systems also let the cameras be connected to the owner’s phone, so that they can view the security footage live or whenever required.

Connecting them all

AI security systems can also be integrated with the smart appliances at home. Amazon and other big companies are investing a lot of money into this. Digital Assistants like Amazon Echo, among others can work with the security systems, allowing them control over the cameras and electronic locks. They can even learn how to recognize the owner’s voices. Canary home security and intelligence is another leading brand which comes with a 1080p HD camera, 90 decibel siren and a built-in climate monitor. It is integrable with the Google Assistant and Amazon’s Alexa.

The Smart and personal assistants build upon the user inputs, using AI and “learn” to work well with the needs of the user. Machine and deep learning systems begin to track the habits of the user.

AI powered home security systems can thus understand when an event seems unusual, after learning from experience and tighten security levels accordingly. This is helpful especially for families who have frequent visitors or travel often, as the home security system reduces the number of false alarms. More modern AI based lock systems use machine learning to keep track of who is using their key to get into your home and when, thereby keeping the owner updated.

Future is in safe hands

Using AI to power home security combats many problems which were prevalent in the past. It also offers many benefits, letting the owners live more comfortably and securely. The systems which use Machine Learning, learn from experience, that is, the more often these systems are used, the more accurate their abilities become.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.


Almost everybody has experienced artificial intelligence of one level or the other by using everyday things around them. The next big thing everybody is looking forward to is revolution in the automated mobility industry. In 2016, Apple Chief Executive Tim Cook described the challenge of building autonomous vehicles as “the mother of all” AI projects.

While big players like Google, Uber, and Tesla are competing with other each and other prominent companies, investing billions to come up with a commercially successful fleet of driverless cars, AI experts believe that it may take many a year before self-driven vehicles can successfully conquer the unpredictability of traffic.

AI plays the main role, as always

An autonomous car can be defined as a vehicle capable of navigating itself without human help, using various sensors to perceive the surrounding environment accurately.  They can make use of a variety of techniques including radar, laser light, GPS, odometry, and computer vision.

Complex algorithms, cameras and LIDAR sensors are made use of to create a digital world that orients the self-driven car on the road and helps identify fellow cyclists, vehicles and pedestrians. It is extremely difficult to design and produce such systems. They must be programmed to cope with an almost limitless number of variables found on roads. The autonomous vehicle industry therefore looks to machine-learning as the basis for autonomous systems. That is because huge amounts of computing power are required to interpret all of the data harvested from a range of sensors and then enact the correct procedures for constantly changing road conditions and traffic situations.

Deep learning and computer vision systems can be ‘trained’ to drive and develop decision-making processes like a human. Humans naturally learn by example and this is exactly what computers are taught to do as well, ‘think like humans’.

What is deep learning? – Deep learning is a method that uses layered machine-learning algorithms to extract structured information from massive data sets. It is a key technology behind driver-less cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. Each self-driven car is programmed to capture data for map generation, deep learning and driving tasks while they move along the traffic.

Autonomous vehicle industry developments

Google launched its self-driving car project in 2009, being one of the first to invest in this stream. Sensing that autonomous vehicle technology can open up a huge market and disrupting the current one, other tech-giants like Intel, IBM and Apple as well as cab hailing companies- Uber and Lyft and car makers have joined the race.

Alphabet’s Waymo, the self-driving technology development company was launched in December 2016. Waymo has been testing its vehicles Arizona for a little more than a year now. Places like California, Michigan, Paris, London, Singapore, Beijing among others regularly witness test-drives by self-driven cars.

The ground reality

While test-drives have become common in these places, the people have not yet adjusted to it. Research conducted by British luxury car maker Land Rover shows that 63% of people mistrust the concept of driverless cars. They are programmed to drive conservatively. While under the right conditions, it can eliminate aspects of human error and unpredictability like speeding, texting, drunken driving, when they move along with human drivers, the same unpredictability can confuse the autonomous cars. This could lead to accidents as well as a general mistrust over the technology. In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman named Elaine Herzberg in Tempe, Arizona. It is clear from regular reporting of accidents that happen during test-drives that autonomous car technology has a long way to go. Even after succeeding to avoid accidents, self-driven cars will have to face more than a decade long transition period, where humans have to accept this technology as well as give up driving.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.


This tech-talk was organized by NULAB wherein a set of four speakers talked about data science and its importance in the business. Ranging from things like jupyter notebooks to AutoML, the series of talks gave the audience a broad insight into what is used today.

Xaltius talked about a very hot topic required and growing in the market today – enhancing customer experience using Data Science and Artificial Intelligence. Using a customer journey map the topic walked through how to understand the pain points of a customer, where AI fits in to address these pain points and how AI can be used broadly across different domains.

We want to thank NULAB for giving us this opportunity to speak.

If you are interested to conduct such workshops and talks for your institution, please get in touch with us


Businesses today collect a tremendous amount of data and this is only going to increase in the coming few years. It has become pivotal for businesses to make sense of the data on an on-going basis through interactive data visualizations. Data visualization expert Stephen Few says, “Numbers have an important story to tell. They rely on you to give them a clear and convincing voice. Any insight worth sharing is probably best shared as a data story.”

Xaltius hosted its first workshop on Data Storytelling which took participants through the story of how to work with data and tell a story around it such that business insights could be derived and be made more explainable.

The workshop engaged participants in a hands-on session with data wrangling, data manipulation and data visualization using real retail data. Data visualization was done on Tableau wherein the participants were exposed to the fundamentals of Tableau including measures, dimensions, calculated fields among many others.

The participants enjoyed the hands-on tremendously and created some wonderful visualizations.

If you are interested to conduct such workshops and talks for your institution, please get in touch with us

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