Applications of Artificial Intelligence (AI) in Healthcare

The ever-evolving technology powered by artificial intelligence is transforming many industries. The healthcare industry is no exception. AI in healthcare has become very prominent. 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. Let us dive deep into understanding what AI is in healthcare by learning about its past, present and future.

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. These developments in AI in the healthcare sector are 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 eyes, 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

One of the major applications of AI in healthcare is the usage of computer vision techniques 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 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 applications of artificial intelligence in healthcare, people still harbor fears of mismanaged care due to a mechanical error. Numerous problems exist with the use of AI in healthcare. The lack of human insight and data privacy issues are other concerns that the industry has to deal with. While technology can support highly trained medical professionals, the chances of it taking over the industry completely remain very low.

The future

In a few years, the market for AI-powered healthcare technologies will exceed 6 billion dollars. 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.

Artificial Intelligence & Autonomous Vehicles – The future of transport

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 (Find out how Xaltius’s Computer Vision Team is building new innovative solutions). 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 (Read our blog on AI vs ML vs DL). 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.

Embracing the growth of AI in the travel and tourism industry

It is the era of digital transformation, where Artificial Intelligence or AI is reaching new dimensions. Hailed as the ‘new electricity, AI, over the last few years has been making rounds in the tourism sector as, a sector which has always celebrated the milestones in technology. Traditional methods of quantitative analysis are being fast replaced by various types of AI such as genetic algorithms, rough sets, grey theory among others. Ranging from smarter virtual assistants and real time chat bots to personalized concierge services, travel websites/ hotels ‘learn’ how to use data to create a better experience.

What is the travel and tourism sector moving towards?

The tourism sector cannot ignore user generated content, peer to peer applications and virtual communities. Due to this, in today’s world, the consumers have access to an enormous amount of information at their fingertips. Recent surveys showed that consumers trusted websites with reviews more than professional guides and tourist agencies. Blogs are considered to be more credible and trustworthy than traditional marketing. Thus, there arises a need to locate, extract and interpret such ‘credible’ blogs and reviews.

Exploring and analyzing such large amounts of data to identify patterns, trends and behaviors allow the tourist agency to predict destinations or more importantly offer customized services. Opinion mining also uses machine learning, a type of AI algorithm to mine text for sentiments. Also known as ‘sentiment analysis’, the system collects and categorizes opinions about a product or a service. AI simplifies the work of the tourism industry professionals, enabling them to concentrate more on improving their profits and customer satisfaction.

AI Developments in the travel and tourism industry

Corporate IT Services and technology consultant Mindtree’s framework ‘Connected Traveller’ uses machine learning to understand the consumer behavior and trends from their travel data. The hotels profit from a better knowledge of consumers through the personal data provided by reservation technology. Machine learning can enable the hotel to offer hyper personalized services.

At Madrid’s Fitur tourism fair, where some of the newest developments were presented, prototype of one hotel showed that tailor made experiences could be offered when customers check in through a mirror equipped with facial recognition. Further, the guests are treated to rooms that complement their habits and desires. Everything in the smart hotel rooms will adjust to the client’s specific needs, including the display of art. All data will be recorded to understand the client’s habits and preferences so that they can enjoy a more customized  experience.

Georgia recently launched a tourism campaign- ‘Emotions are Georgia’, where AI was used to detect genuine tourist emotions from over 70,00,000 posts of travellers to create an ‘emotional’ and accurate guidebook of the country. Giorgi Chogovadze, Head of Georgian National Tourism Administration, believes that these posts are “straightforward, heartfelt and uncensored reviews written by regular tourists who have actually visited the country.”

French technology consultancy Altran developed a prototype aimed at luxury hotels, where ‘smart’ doors are opened and closed through  Whatsapp. The guests can order a pizza in 40 languages through speech recognition technology and the mattress is equipped with sensors that allow the hotel to serve coffee at the right time.

The hotel chain Palladium has replaced paper brochures with virtual reality headsets.

From all these examples, we find that customization is given the utmost preference, as machine learning allows the travel agencies, hotels and attractions to match the desires, habits and preferences of the tourists with the offered services or products. Hence a personalized menu, preferred room temperature, lighting and other services offered helps increase customer satisfaction immensely.

Where is AI taking the travel and tourism industry?

Artificial Intelligence is creating dramatic technological changes, which will also revolutionize the way people travel. Artificial intelligence is already helping major players in the tourism industry in decision making, managing predictive maintenance and handling disruptions like weather conditions. The number of travel agencies and hotels using AI will increase tenfold in the future, just like the present increase in the number of consumers using virtual assistants like Siri and Alexa, making them household names. Embracing Artificial Intelligence will benefit travellers as well as tour operators, aviation sector and even hospitality. With the advanced progress that technology is making, radical transformation is all set to happen in the way people live and travel.

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.

{Python Training} – (Code For Asla & Xaltius)

This November of 2017, Xaltius in collaboration with Code For Asia took to conduct a python basics training workshop called CodeCircles for beginners and those interested to hone their python skills.

The training was largely attended by an avid audience consisting of PMETs, college students and working professionals, all of whom were eager to get their hands dirty and their programming skills in python up to speed.

The course was structured over a period of one month and included theoretical and rigorous hands on sessions to get the audience as comfortable on python as quickly as possible. The course content itself ranged from the very basics of code structure and data types to data visualization, file handling and image processing on python, all of them done with the help of jupyter notebooks. One of the main aims of the workshops was also to help relate business cases to “how programming could help solve that”, either in terms of simple data manipulation, statistical calculations or complex data science algorithms.

Through this workshop the audience took with them basics of programming, the various operations which could be done using python and most importantly, how all of the above could be of use to them either in their projects or business. As for the entire team of Xaltius and Code For Asia, both received a tremendous positive response.