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Future of AI in Healthcare (Part 2): Preventing Diabetes

Note: This article is the second part of a series (see Part 1), in which Savvycom Team will discuss future of AI in healthcare – battling the world deadliest diseases.

According to Wikipedia, the first clinical description of this illness was noted down by Aretaeus during the 1st century CE. At that time, Diabetes was only described as:

A disease that caused an excessive amount of sweet urine which passed through the kidneys.

Not much was known about this illness. In fact, diabetes was quite rare. No one would have imagen that one day, diabetes will become the biggest epidemic in human history – affecting 415 million people worldwide by 2018.

Standing at number 4 In WHO’s TOP 10 deadliest disease worldwide, Diabetes is considered a progressive disease. As stated by the US National Library of Medicine, premature death caused by diabetes results in about 12 to 14 years of life lost. The patients and their family also incur medical costs that are 2 to 5 times higher than those without Diabetes. The annual direct health care costs of diabetes worldwide, for people in the 20–79 age groups, are estimated to be as much as 286 billion.

The world seems daunting after all of those statistics. But, with the rise of Artificial Intelligence, advanced medical protocol seems to be on the horizon.

Is there a future of AI in healthcare for Diabetes patients?

1. Diabetes: A world epidemic

According to WHO, Diabetes is defined as ‘a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces’

So, what is insulin?
To answer that, let’s first start with another question: What is glucose?

Glucose is a medical term for the sugar created by the digestive system as it consuming and breaking down foods. This sugar is the fuel for the cells – much like gasoline to cars. Without gasoline, cars cannot run. Without glucose, cells cannot continue living.

However, an average human body contains more than 37.2 trillion cells, spread across a vast area. Therefore, turning food into glucose is not the final step. That glucose still has to get to every single cell in one’s body. And that’s where insulin came in.

Insulin acts as the pump that transfers gasoline to the car’s fuel tank. Since cells have ‘locks’ that are called insulin receptors, insulin fits into these locks like a key. When insulin opens the locks, glucose is allowed to enter the house of cells.

To be more specific, the pancreas is genetically coded to produced different amounts of insulin depending on how much glucose is in the bloodstream. When a person goes about their day, not eating much, it releases just a bit to keep things regulated. But during food consumption and processing, it generates a burst of insulin in response.

Hence, insulin is vital factors to keep glucose at balance level and let one’s body operate at its optimal level. Having too much or too little glucose for a substantial period of time and one’s body starts running into complications – including the 2 types of diabetes.

Remember the ‘lock-key’ metaphor earlier?

Future of AI in Healthcare

Type 1 vs. Type 2 Diabetes | Healthstyle

With type 1, there is no key. The patient’s body makes little to no insulin because the beta cells in the pancreas that make insulin are mistakenly destroyed by the body’s own immune system as it was fighting infection. This type is usually diagnosed in children and young adults.

Type 2 is more common. With this type, there are faults in the key itself. The beta cells in the pancreas produce insulin, but not enough to keep blood sugar levels within a normal range or the body doesn’t respond properly to insulin. Without enough insulin to direct the flows of glucose, the glucose is left in the blood. This is what happens when someone is having “high blood sugar”.

According to WebMD, early warning symptoms of Diabetes include small incidents such as increased thirst or hunger, frequent urination, unexplained weight loss, fatigue, blurred vision, and headaches. Leave it for a longer time, the patient will be exposed for 3 times more risk of experiencing heart attacks, strokes, nerve damages, infections that might lead to lower limb amputation, blindness, and kidney failure.

As you can see, thanks to science progressions and enormous data of health records, we do have a certain level of knowledge in regard to how the disease progress. However, not much is known about the specific causes of diabetes. Scientists think that while type 1 diabetes is caused by sudden environmental factors (such as virus or infections), type 2 diabetes is created by a more collective group of triggers: obesity, lack of physical activities, genes and family history.

2. Is there a cure?

This is going to sound very similar for every disease mentioned in this series: there is currently no cure for diabetes.

Scientist and doctors in related fields have been trying for decades. With Type 1, clinical attempts focus on either replacing the damaged pancreas with a healthy one (through islet cell or pancreas transplant) or targeting the immune system in an effort to stave further damage to the pancreas. However, these efforts have experienced several shortcomings. Not only that donors are in very short supply, systematic reviews also find transplant results themselves tend to vary significantly. With regard to treatments targeting the immune system, the results remain blunt and non-specific.

With type 2, it has been noticed that the number of patient rises along the global rate of obesity and metabolic syndrome – a cluster of conditions related to blood sugar, excess fat and abnormal cholesterol level. This has fueled an increase in weight loss surgical interventions,. However, depending on the country and insurance plans, such surgery can be costly. They’re also not risk-free with risks varying greatly depending on the person’s overall health profile and age as well as skill and experience of the surgeon.

In the meantime, tremendous interest lies in the usage of different types of stem cell to regenerate the pancreas. This has been applied for both type 1 and type 2 diabetes in recent years with mixed results and limitations. For example, later stages of diabetes’ patients are not good candidates for stem cell therapy.

3. Future of AI in Healthcare: Diagnosis and Effective Control

There is definitely hope since Diabetes can be controlled through effective medication and a healthier lifestyle. What’s vital here is the patient and doctors’ acknowledgment of the current situation. This means that early diagnosis, non-invasive test, and effective maintenance protocol are the key factors. With that being said, AI’s future in healthcare – particularly in Diabetes can be divided into three main categories:

3.1.Non-invasive early diagnosis:

  • How: According to WHO, although detection is improving, the delay from disease onset to actual diagnosis may exceed to 10 years. Contributed reasons to this issue include the subtleness of early symptoms along with the complicated process of diagnosing – which involves a range of actors following the Finnish Diabetes Risk Score. As this method requires human intervention and expertise, it may be exposed to human errors.
  • Highlighted projects: According to Reuter, one of the most influential complications of diabetes is diabetic retinopathy (DR) – damages in the eye blood vessels and vision loss. IDx-DR, a software produced by an Iowa-based company, utilizes AI software to self-assess the eye images taken by a retinal camera. After a series of comparison to a provided database, the software tells the doctor that the patient either has more than mild DR and should be referred to eye-care professionals or is “negative” and should be rescreened in 12 months.

    Future of AI in Healthcare

    Future of AI in Healthcare: IDx-DR example | Intro Wellness

    Result: In a clinical trial, IDx-DR was able to correctly identify the presence of more than mild diabetic retinopathy 87% of the time and identify those who did not have more than mild disease 89% of the time. It has now received the FDA’s authorization to provide screening decision without the need or assistant of a clinical.

3.2. Non-invasive Glucose Monitoring Systems:

  • How: Once diagnosed, frequent adjustments of the insulin treatment plan are crucial for successfully achieving glucose controls goals. Not only is insulin optimization calculation is a time-consuming process, it also demands constant updating data from a board range of devices – glucose monitoring devices, insulin dose regimens, diet tracking calendar, exercise diary. Thus, traditional physicist only gets to see their patient once every few months. With an AI platform, machine learning algorithms can help automate the process of monitoring blood sugar levels and recommend adjustments in care.
  • Highlighted projects: Founded in 2014, DreaMed Advisor cloud-based analytics platform uses machine learning to recommend optimal insulin dosages to maintain balanced glucose levels. For example, data from diabetes management systems are transmitted to the cloud. The patterns derived from analysis through its event detections and learning algorithm are referenced to provide automated recommendations for insulin dosing and treatment plan – in real time. Doctors can then access the cumulative data from the cloud and learn the patient’s unique habits and needs.

    Future of AI in Healthcare

    Future of AI in Healthcare: DreaMed Advisor | DreaMed Advisor’s Youtube

    Result: The U.S. National Library of Medicine indicates that DreaMed began recruiting participants in December 2016 for an evaluation study in children and adolescents with type 1 diabetes. The result will be released in late 2018.

3.3. Nutrition Coaching:

  • How: One of the biggest parts in taking control of this life-long illness is the patient’s diet. As one’s body experiencing internal chemical imbalance, that person needs to watch their intake in sugar, fat, protein and carb index. However, there isn’t one specific “diabetes diet”. Doctors need to work closely with their patient to customize their specific meal plan – which is, of course, demand extensive knowledge in the nutrition. With machine learning, AI can help recommend meal options based on the specific diet criteria of the user.
  • Highlighted projects: Founded in 2014, California-based Suggestic is taking a nutrition-focused approach to helping diabetics manage their health. The platform is built on an extensive database of over 1 million recipes and 500,000 restaurant menus. This data is used to train algorithms to recognize which food selections complement specific diets. The platform also uses an Adherence Score scale – ranging from green (optimal) to red (least optimal) to determine how well a meal option fits with a user’s diet.

    Future of AI in Healthcare

    Future of AI in Healthcare: Suggestic Interface | Suggestic

    Result: This app, even though only being made available for iOS devices, scores 4.8 stars out of over 150 reviews. They are also in partnership with The Institution for Functional medicine and Health Coach Institute.

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On one hand, we have yet to understand the root causes of Diabetes. But on the other hand, we have come up with various protocol to ensure that the patients can still live a long and healthy life through disease-modifying treatments and lifestyle alterations. The most important thing at this point is to extensively understand the stages of diabetes and what implications they may have on the people’s life.

Future of AI in Healthcare is bright. Hence, healthcare institutions need to catch up with this first wave of AI development, not only to remain sustainable and profitable but to ensure that they are doing their parts in the making of needed medical progression. It is not an easy task, and that’s why we are here help.

Striving to be a key advocate for the future of AI in healthcare industry worldwide, Savvycom founded a new AI Lab back in March 2018. Leading by Dr. Long Tran, our team have developed three AI applications (Facial Recognition, Object Identification, and AskFred – an AI Chatbot) and are now in the process of commercialization. These technologies are developed with the hope that it will become the foundation of our future healthcare-related products. For example, AskFred can be used as a personal assistant to Diabetes patient – providing answers to the patient questions with the micro/macro nutrition components in each meal.

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AI, Machine Learning, and Deep Learning: The simplified distinction

AI, Machine Learning, and Deep Learning are often being used as synonyms in IT-related articles and conversations. But they shouldn’t be.

1. Artificial Intelligence: The dream

AI assistant can provide a huge momentum for the economy.

AI assistant can provide a huge momentum for the economy.

AI was first defined by John McCarney in 1956 as computer systems being able to mimic human characteristics and perform tasks normally requiring human intelligence. An AI in its creator’s perfect vision will be able to plan, to understand natural human language, to recognizing objects or sound, along with to learn and acquiring knowledge.

There are many ways for us to distinguish among various types of AI. However, for the sake of simplification, the mass audience tends to go with the 2-tier division, including Narrow AI and General AI.

Narrow AI (also known as Weak AI) is the artificial intelligence that can perform one human-like task quite well but lacks in others. The most prominent examples of this AI’s type is probably Siri of Apple or Alexa of Amazon. ‘They’, although very sophisticated, function within a limited pre-coded spectrum, having no intelligence of ‘their own’, no ability to learn by ‘themselves’.

SIRI Interface.

SIRI Interface.

On the other hand, General AI (also known as Strong AI) is the artificial intelligence that carries all characteristics of human intelligence. In fact, in order to be considered as a General AI, the machine must past 4 specific tests – as stated by the Machine Intelligence Research Institute:

  1. The Turing test: A human evaluator would judge natural language conversations – through text-channel only – between a human and a machine. All participants would be in separate rooms from each other. If the evaluator cannot reliably tell the machine from the human, the machine has passed the test.
  2. The Coffee test: The machine must go into an average American house and find out how to make coffee. It will have to identify the coffee machine, figure out what button does what, find the cabinet that the coffee is stored in, and so on
  3. The Robot College Student test: The machine must enroll in a human university, take classes in the same manner as a human and graduate with a degree as a human.
  4. The Employment test: The machine must have an economically important job and perform in it well enough to pass special vocational exams designed for that job.

So far, AGI is still a science fiction character. Right now, the closest inspiration for what an AGI can be like is Sophia – a social humanoid robot developed by Hong Kong-based company Hanson Robotics.

A picture of Sophia the Robot.

A picture of Sophia the Robot.

2. Machine Learning: First sign of Artificial Intelligence’s Cognition

Machine learning, in short, is one of the mean to achieve AI.

As explained by Arthur Samuel in 1959, it is a field of computer science that uses the statistical technique to give computers the ability to progressively improve their performances on a specific task without being explicitly programmed. In other words, rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “taught” using large amounts of data and algorithms that give it the ability to learn how to perform the task.

A prominent example of this technology real-life application is the Facebook’s face recognition feature. In the past, the user constantly ‘fed’ the website with a large number of pictures. In some, he even tagged his friends – linking the friends’ faces to their personal accounts. As time went by, Facebook learned how those particular friends look like, even from different angles. Hence, that user can now upload another picture and let the machine automatically identify the person in that picture. It is not always right, but it is closed.

Face Recognition technology | PYMNTS

Face Recognition technology | PYMNTS

3. Deep Learning: Human imprint on Artificial Intelligence

Deep learning is one of many ways to approach machine learning.

First coined by Rina Dechter in 1986, deep learning is based on the idea of Artificial Neural Networks (also known as ANN) – which is vaguely inspired by the human biological nervous system, or to put it simply, the human brain.

In the brain, the action of ‘learning’ relies on the interconnecting nature of many neurons. One neuron firing inspires others to fire.

In the ANN’s deep learning technology, there are also ‘neurons’. But unlike a human brain where any neuron can connect to any others within a certain physical distance, the ANN has discrete layers and directions of data reproduction. Each layer contains a number of neurons. Each neuron holds a score (also known as an activation) which is responsible for assigning a weighting to its input, depends on how correct or incorrect it is relative to the task being performed. The final output is then determined by the total of those weightings.

A simplified diagram of Artificial Neural Network's model

A simplified diagram of Artificial Neural Network’s model

For example, a human hand-writes a number – which as we know, can be varied in shape from the standard keyboard numbers. That human then takes a picture, chop it up into a bunch of tiles that are inputted into the ANN. The number of ANN’s ‘neuron’ in the first layer depends on the number of tiles. When certain first-layer neurons have a high score – indicating the similarity of the hand-written number with a number in the ANN programme, they activate certain neurons in the second layers. The algorithm moves forward with each layers doing their job until the final layer, where the output is produced: the computer learn what is the number that was being written. The system might be 79% confident the image is a 9, 12% confident it’s a 3, 2% it’s a 1, and so on.

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In conclusion, all three fields are closely entwined but different in definitions. Deep Learning has enabled numerous practical applications of Machine Learning and by extension, the overall AI field. It breaks down tasks and processes in ways that make all kinds of machine assistant seem more possible than ever. Driverless cars, virtual butler or simply better movie recommendations – all are on the horizon.

Acknowledging this thriving potential, Savvycom officially launched a new AI Lab back in March 2018. Leading by Dr. Long Tran, our team have introduced two AI applications:

  1. Visual Search with AI: an Artificial Intelligence Visual Search tool to utilize the big E-commerce database of thousands of products and amplify the E-commerce experiences.
  2. AskFred – AI Chatbot: an All-in-One Artificial Intelligence Chatbot that acts as a personal assistant in both workplace and daily life interaction, thanks to its machine learning model and personality test feature.

We are confident that with our talented experts and engineers from top universities in the US and Europe, we can capture and satisfy the client’s growing demand for innovative solutions.

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How Machine Learning and AI will transform business intelligence and analytics!

Machine learning and AI advances in five areas will ease data prep, discovery, analysis, prediction, and data-driven decision making.

For 10 years the prevailing trend in business intelligence (BI) and analytics has been the move toward self-service. That’s about to change. In 2018 and beyond, we’ll see a growing list of what many call “smart” capabilities powered by machine learning (ML) and artificial intelligence (AI). These features are sure to help us move beyond the limits of the self-service era.
savvycom-how-ml-and-ai-will-transform-business-intelligence-and-analytics
Expect a steady drumbeat of announcements throughout 2018 and beyond about ML applied to tasks including cleaning and combining data, discovering new data, and suggesting new combinations of data that could, in turn, uncover important insights. Non-technical business users will appreciate ML-powered suggestions on best-fit data visualizations. Automated modelling features, meanwhile, will help non-technical business users tap into the power of predictive analytics.
Some of these capabilities are already starting to appear. For example, natural language (NL) querying based on keywords available in column headers has been with us for years. Some vendors are now using more advanced NL capabilities that can discern nuances and intent in complete sentences (whether typed or translated from voice with speech-to-text capabilities). On the cutting edge, systems are starting to retain the context of queries; instead of asking one isolated question at a time, you’ll have a responsive dialogue with the data, drilling down and exploring from an initial query.
Savvycom-Is modern AI intelligent enough?
Of course, many business users are more interested in action and outcomes than interpreting reports, dashboards, and data visualization. These are the users more likely to take advantage of the growing list of smart, ML- and AI-powered prescriptive applications emerging. Here’s where the context of decisions is built into business applications for sales, marketing, HR, supply chain, logistics, and more. In these cases, the data analysis can be tuned to deliver recommended next steps or even to automate actions sure to lead to desired outcomes.
These emerging capabilities will make BI, analytics, and data-driven decision-making that much more accessible, understandable, and actionable for non-technical business users, but embracing the new won’t be as easy as waving a magic wand. I’ve spoken to practitioners who were surprised and dismayed to see employees responding to ML- and AI-powered recommendations in unexpected ways. Salespeople, for example, sometimes stubbornly pursue leads deemed as less than promising by predictive scores. Here’s where change management will be crucial. As I explain in my report, delivering transparent and explainable AI that instils trust will be crucial to making smart systems succeed.
Read more:

 
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Addressing 4 systematic AI problems

Reality is suddenly dawning for many who believe that the current AI tools in the market are a silver bullet to solve everything from cancer to your toughest business problems. In spite of such benefits, many AI entrepreneur and business have faced significant systematic AI problems. A recent TechCrunch article citing a study from global investment bank Jefferies, points out that IBM Watson is not providing the promised business value to their clients. How could that be?

Is modern AI intelligent enough?

It might be because it, like other similar AI tools in the market today, is not so cognitive after all. Simply speaking, Watson technology is comprised of a bag of answers to a bag of questions for a particular subject, and a Machine Learning (ML) mechanism to best correlate answers to questions. Watson neither understands the meaning of the question or the meaning of the words it uses. And one can only go so far without understanding.
Read more about AI:

Modern AI is being incorrectly equated in the industry to ML, which has caused at least four systemic problems:

1. NEED TO HAVE LOTS OF DATA

  • With small amounts of data, there is no model
  • If our data is biased we train biased models
  • Only the companies with access to lots of data can create the best models. It is difficult for startups without access to data to create healthy competition

2. LACK OF TRANSPARENCY

We do not understand why the AI models arrive to decisions and predictions and therefore can’t be trusted

3. IT DOESN’T ALWAYS WORK

  • When it doesn’t work we don’t know why or how to fix it
  • Difficult to reach high levels of accuracy
  • Uncertain time to train new models

4. THE FOCUS IS ON TECHNOLOGY INSTEAD OF ON SOLVING BUSINESS PROBLEMS

The current AI conversation is more about what can be solved in a company with a chatbot, or Robotic Process Automation, or what can be classified or predicted. Instead focus should be on the main pain points of the company that will bring about the most transformational value, using AI and not AI technologies, to arrive to the most simple and elegant solution.
Savvycom-Is modern AI intelligent enough?
I believe that the solution to the 4 systemic AI problems of modern AI resides in the integration of technologies. For sensor interpretation and integration, this means using Knowledge Representation technologies, including Qualitative Modeling, Ontologies, Web Semantics, and Edge Computing. And for reasoning, using inference tools, including Qualitative Reasoning, and Decision Trees.
The only way to get transparency is by going back to the basics of understanding the problem, the words used, and the operations to automate. “Knowledge is power” can be true thanks to AI. The transformation of data/information into knowledge/wisdom that holistic AI provides will allow real transformation in corporations.
Let’s focus on defining the AI journey of our companies. Solving the main current pain points with the best combination of technology will progressively transform companies to become more efficient. And let’s do it in a responsible way: paying attention to the type of solutions that we develop – only to enhance human capabilities and making sure that we find better professional alternatives for those whose job will be displaced by AI (that finally will be all of us).
Read more: Part II – The solution to the 4 systemic problems of modern AI
 

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Savvycom Launches New Artificial Intelligence (AI) Lab

The next innovation AI Lab in Vietnam, with advanced tech as its main driving force

Clients rotate to the new, they demand both innovative solutions for their toughest challenges and the technical know-how to effectively deliver those solutions. That’s the idea behind our AI Lab – where our experts and engineers obtained PhD degree in AI and data analysts from the top university in the US, Europe… in Vietnam, can research and apply AI technologies as well as focus on prototyping AI applications for client problems across various industries.
Read more:

savvycom-ai-lab
Savvycom AI Lab is part of a complete range of software product development that helps quality engineering professionals be a catalyst for speed, agility and business performance while achieving radical productivity. Savvycom serves over 100 international clients across the US, Australia, Singapore and other European countries. It is recognized as the Top 30 Global App Developers by Clutch.
At AI Lab, Savvycom technology professionals also gain unique opportunities to learn, hone and master the fast-evolving skills they need to serve clients’ future digital needs.
savvycom-ai-lab-innovation

“Our expertise includes Intelligence Automation, Blockchain, Machine Learning and Data Analysis”

Shoppers are facing Discovery problems, which are lots of options for them to choose on sellers’ websites. To deal with these issues, Savvycom introduces a new search tool which can quickly process images and identify specific objects within the image, then generate visually similar results. We call it “Visual Search” – one of AI Lab applications.
 
visual-search-savvycom-applications

“The future of visual search engines is most likely to be a shopper’s paradise in
the right retailer’s hands”

Research showed that Visual-oriented Search engines were interested in nearly 70% of young clients. Visual search is very useful in the E-commerce industry that helps shoppers decrease the number of choices and find the products they want to purchase efficiently.
Enter the world of AI Lab and learn what drives our passionate team of researchers!
 

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What are chatbots?

What are chatbots (bots)? Bots are used as virtual assistants which help to chat and build relationships with users without needing to speak to another human. They’re in Skype, Facebook Messenger and here’s what you need to know.

What are chatbots?

Chatbots are computer assistants. They appear in your messaging apps like Facebook Messenger and Skype. It has been used for booking flights, hotels, ordering takeaway and much, much more.

The chatbots we’re talking about here are essentially virtual assistants, much like Alexa, Siri and Cortana, but they communicate via text rather than speech. Cortana and Google Assistant already do this, of course.

Modern chatbots

The new chatbots are much like ELIZA and Eugene Goostman in that you can chat to them – by typing – and they will respond with sensible, intelligent answers. While mere chatbots exist (and have fooled many a human) the next generation will act more like personal assistants, doing everything from handling your Amazon returns to booking flights and ordering your lunch. They go beyond the capabilities of Siri and Google Now, although in many respects they are similar.

There are plenty of chat bots in Skype, including IFTTT, SkyScanner, Hipmunk and Stubhub.

Microsoft spent a considerable portion of its Build keynote last year talking about bots and how they would make your life easier. In one demo, a bot was used to order a pizza. It sounds simple, but it highlighted how much easier it was to give your order through a bot rather than firing up an app, navigating through menus and selecting options.

Chatbots: The future

It’s extremely likely the Apple is working hard behind the scenes to make Siri do this, because its capabilities and intelligence haven’t significantly improved since it launched. But more than anything, it needs to make Siri work universally rather than only kit designed for it.

Of course, bots will be limited by the platforms on which they run. So if you’re using Facebook M or Skype on an iPhone, it will be limited by what iOS can do, or what Apple will let it do. But Google is also busy building bots, and it will be fascinating to see what appears in both Android N and iOS 10.

Over the next few months we can expect bots to start appearing in the big messaging apps, and we can look forward to being able to order that lunchtime pizza in a snap.

 
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How AI And Chatbots Are Strengthening The Customer Experience?

Artificial Intelligence (AI) is dramatically changing business, and chatbots, fueled by AI, are becoming a viable customer service channel. The best ones deliver a customer experience (CX) in which customers cannot tell if they are communicating with a human or a computer. AI has come a long way in recognizing the content – and context – of customers’ requests and questions.

Typically, customer service chatbots answer questions based on key words. The most basic systems are actually document retrieval systems. Sometimes this is frustrating. Think of the times you may have asked Siri or Alexa a question and received the wrong answer. The computer recognizes key words but may not recognize the context in which they are being used. In other words, the computer doesn’t recognize the way people naturally speak. This causes the customer great frustration. However, these systems (including Siri and Alexa) have come a long way and continue to improve.

For at least the foreseeable future, chatbots won’t be replacing humans in contact center jobs. At this point, chatbots will only replace some of the tasks that people are now handling – especially lower-level requests, questions and complaints. The best chatbot systems can recognize customer frustration and switch the interaction to a human in the company’s support center. That said, chatbots are on their way to mainstream acceptance. Here are four ways AI and chatbots are creating a major impact in the customer service

  • The chatbot never sleeps
  • The chatbot won’t make you wait
  • Personalizing the customer experience
  • Chatbots help to build relationships

Also read: What are chatbots

1. The chatbot never sleeps:

Customer service is all about convenience, which includes 24/7 customer support. A cost-efficient, yet powerful way to provide basic support is through the never-sleeping chatbot. An excellent example of this. In the banking industry, chatbots are trained by using historical conversations and can perform some of the same tasks as a live support center rep such as correcting an invoice, answering basic questions about account balances and more.

Customers receive the same level of service they would get from the support rep. The chatbot can recognize human emotions such as anger, confusion, fear and joy. And, as mentioned above, if the chatbot detects that the customer is angry, upset or frustrated, it will seamlessly transfer the interaction to a human to take over and finish assisting the customer.

2. The chatbot won’t make you wait:

The concept of on-hold music is a friction point in customer service. With chatbots, you no longer have to wait for the next agent.

3. Personalizing the customer experience:

Chatbots excel at collecting customer data from support interactions. After all, it’s the computer that’s doing the work. The advantage is that live support agents can use this information to personalize their interactions with customers. Chatbots serve as virtual assistants that can feed customer data to the agent in real time, so the agent can give the customer good information and solutions based on current needs as well as past interactions with the company.

4. Chatbots make friends and build relationships:

Most companies wish their agents had more time to make outbound, proactive contact with their customers. Chatbots are there to help, and in some ways, they are revolutionizing the way brands stay in touch with their customers. Whether it’s a simple email or text on a customer’s birthday, or a quick check-in to ask if they are enjoying the brand’s product or service, chatbots are helping to foster brand loyalty.

This may sound counterintuitive, but the most sophisticated chatbots can provide a more human experience than an actual human. They don’t have bad days and they don’t get frustrated by typical customers.

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How AI and ML are Redesigning Healthcare

The Healthcare Technology Industry in the Healthcare Sector includes companies providing information technology services primarily to healthcare providers in order to help people live a longer, healthier, happier life.

A range of new technologies is fusing the physical, digital and biological worlds, impacting all disciplines, economies, and industries. Among them, Artificial Intelligence (AI) and Machine Learning (ML) have an unimaginable potential. They are transforming the healthcare industry in three areas:

how_ai_and_ml_are_redesigning_healthcare_1
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Top 8 Healthcare Technologies to Look Forward in 2017

Like 2016, 2017 is shaping up to be one of the biggest years for healthcare technologies ever, with advancements in cures, research, and treatments. It can’t be denied that technology is the driving force behind improvements in healthcare.

The growing adoption of various healthcare IT solutions allows healthcare providers to meet the heightened regulatory requirements for patient care and safety. In this world, many are left wondering what to expect in 2017. Here are just top 8 medical technologies and some startups in each category that Savvycom team are most looking forward to healthcare.

A look forward to healthcare in 2017: Top 8 technologies

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15 Data Analytics Trends That Will Dominate 2017

2016 was a landmark year for data analytics with more organizations storing, processing, and extracting value from data of all forms and sizes. In 2017, systems that support large volumes of both structured and unstructured data will continue to rise.

Analytics without application to an actionable strategy is meaningless”- Mike Grigsby

There’s always something new on the horizon, and we can’t help but wait and wonder what technological marvels are coming next. Savvycom team has outlined the predictions for what 2017 will bring data and analytics.

15-analytic-trends-in-2017

15-analytic-trends-in-2017

1, The emergence of the data engineer
2, Artificial intelligence (AI) is back in vogue.
3, Big data for governance or competitive advantage.
4, Companies focus on business driven applications to avoid data lakes from becoming swamps
5, Data agility separates winners and losers.
6, Blockchain transforms select financial services applications
7, Machine learning maximizes microservices impact.
8, Intelligent networks lead to the rise of data clouds.
9, Real-time machine learning and analytics at the edge.
10, More pre-emptive analytics: from post-event to real-time, pre-event analysis and action.
11, Ubiquity of connected modern data applications.
12, Data will be everyone’s product.
13, The emergence of the data engineer.
14, Security: Growth of IoT leads to blurrred lines.
15, Hybrid wins, thanks to certain enterprise-ready cloud applications.

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