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The Race to Save Indigenous Languages, Using Automatic Speech Recognition

By Tanner Stening for News@Northeastern

Michael Running Wolf still has that old TI-89 graphing calculator he used in high school that helped propel his interest in technology. 

“Back then, my teachers saw I was really interested in it,” says Running Wolf, clinical instructor of computer science at Northeastern University. “Actually a couple of them printed out hundreds of pages of instructions for me on how to code” the device so that it could play games. 

What Running Wolf, who grew up in a remote Cheyenne village in Birney, Montana, didn’t realize at the time, poring over the stack of printouts at home by the light of kerosene lamps, was that he was actually teaching himself basic programming.

“I thought I was just learning how to put computer games on my calculator,” Running Wolf says with a laugh. 

But it hadn’t been his first encounter with technology. Growing up in the windy plains near the Northern Cheyenne Indian Reservation, Running Wolf says that although his family—which is part Cheyenne, part Lakota—didn’t have daily access to running water or electricity, sometimes, when the winds died down, the power would flicker on, and he’d plug in his Atari console and play games with his sisters. 

These early experiences would spur forward a lifelong interest in computers, artificial intelligence, and software engineering that Running Wolf is now harnessing to help reawaken endangered indigenous languages in North and South America, some of which are so critically at risk of extinction that their tallies of living native speakers have dwindled into the single digits. 

Running Wolf’s goal is to develop methods for documenting and maintaining these early languages through automatic speech recognition software, helping to keep them “alive” and well-documented. It would be a process, he says, that tribal and indigenous communities could use to supplement their own language reclamation efforts, which have intensified in recent years amid the threats facing languages. 

“The grandiose plan, the far-off dream, is we can create technology to not only preserve, but reclaim languages,” says Running Wolf, who teaches computer science at Northeastern’s Vancouver campus. “Preservation isn’t what we want. That’s like taking something and embalming it and putting it in a museum. Languages are living things.”

The better thing to say is that they’ve “gone to sleep,” Running Wolf says. 

And the threats to indigenous languages are real. Of the roughly 6,700 languages spoken in the world, about 40 percent are in danger of atrophying out of existence forever, according to UNESCO Atlas of Languages in Danger. The loss of these languages also represents the loss of whole systems of knowledge unique to a culture, and the ability to transmit that knowledge across generations.

While the situation appears dire—and is, in many cases—Running Wolf says nearly every Native American tribe is engaged in language reclamation efforts. In New England, one notable tribe doing so is the Mashpee Wampanoag Tribe, whose native tongue is now being taught in public schools on Cape Cod, Massachusetts. 

But the problem, he says, is that in the ever-evolving field of computational linguistics, little research has been devoted to Native American languages. This is partially due to a lack of linguistic data, but it is also because many native languages are “polysynthetic,” meaning they contain words that comprise many morphemes, which are the smallest units of meaning in language, Running Wolf says. 

Polysynthetic languages often have very long words—words that can mean an entire sentence, or denote a sentence’s worth of meaning. 

Further complicating the effort is the fact that many Native American languages don’t have an orthography, or an alphabet, he says. In terms of what languages need to keep them afloat, Running Wolf maintains that orthographies are not vital. Many indigenous languages have survived through a strong oral tradition in lieu of a robust written one.

But for scholars looking to build databases and transcription methods, like Running Wolf, written texts are important to filling in the gaps. What’s holding researchers back from building automatic speech recognition for indigenous languages is precisely that there is a lack of audio and textual data available to them.

Using hundreds of hours of audio from various tribes, Running Wolf has managed to produce some rudimentary results. So far, the automatic speech recognition software he and his team have developed can recognize single, simple words from some of the indigenous languages they have data for. 

“Right now, we’re building a corpus of audio and texts to start showing early results,” Running Wolf says. 

Importantly, he says, “I think we have an approach that’s scientifically sound.”

Eventually, Running Wolf says he hopes to create a way for tribes to provide their youth with tools to learn these ancient languages by way of technological immersion—through things like augmented or virtual reality, he says. 

Some of these technologies are already under development by Running Wolf and his team, made up of a linguist, a data scientist, a machine learning engineer, and his wife, who used to be a program manager, among others. All of the ongoing research and development is being done in consultation with numerous tribal communities, Running Wolf says.

“It’s all coming from the people,” he says. “They want to work with us, and we’re doing the best to respect their knowledge systems.”

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Physician burnout in healthcare: Quo vadis?

By Ifran Khan for Fast Company

Burnout was included as an occupational phenomenon in the International Classification of Diseases (ICD-11) by the World Health Organization in 2019.

Today, burnout is prevalent in the forms of emotional exhaustion, personal, and professional disengagement and a low sense of accomplishment. While cases of physician fatigue continue to rise, some healthcare companies are looking to technology as a driver of efficiency. Could technology pave the way to better working conditions in healthcare?

While advanced technologies like AI cannot solve the issue on their own, data-driven decision-making could alleviate some operational challenges. Based on my experience in the industry, here are some tools and strategies healthcare companies can put into practice to try and reduce physician burnout.


Clinical decision support (CDS) tools help sift through copious amounts of digital data to catch potential medical problems and alert providers about risky medication interactions. To help reduce fatigue, CDS systems can be used to integrate decision-making aids and channel accurate information on a single platform. For example, they can be used to get the correct information (evidence-based guidance) to the correct people (the care team and patient) through the correct channels (electronic health record and patient portal) in the correct intervention (order sets, flow sheets or dashboards) at the correct points (for workflow-based decision making).

When integrated with electronic health records (EHRs) to merge with existing data sets, CDS systems can automate data collection on vital life signs and alerts to aid physicians in improving patient care and outcomes.


Companies can use AI-enabled speech recognition solutions to reduce “click fatigue” by interpreting and converting human voice into text. When used by physicians to efficiently translate speech to text, these intelligent assistants can reduce effort and error in documentation workflows.

With the help of speech recognition through AI and machine learning, real-time automated medical transcription software can help alleviate physician workload, ultimately addressing burnout. Data collected from dictation technology can be seamlessly added to patient digital files and built into CDS systems. Acting as a virtual onsite scribe, this ambient technology can capture every word in the physician-patient encounter without taking the physician’s attention off their patient.


Resource-poor technologies sometimes used in telehealth often lack the bandwidth to transmit physiological data and medical images — and their constant usage can lead to physician distress.

In radiology, advanced imaging through computer-aided ultrasounds can reduce the need for human intervention. Offering a quantitative assessment through deep analytics and machine learning, AI recognizes complex patterns in data imaging, aiding the physician with the diagnosis.


Upgrading the digitized medical record system, automating the documentation process, and augmenting the medical transcription are the foremost benefits of natural language processing (NLP)-enabled software. These tools can reduce administrative burdens on physicians by analyzing and extracting unstructured clinical data to document relevant points in a structured manner. That avoids the instance of under-coding and streamlines the way medical coders extract diagnostic and clinical data, enhancing value-based care.


Advanced medical technologies can significantly reduce physician fatigue, but they must be tailored to the implementation environment. That reduces physician-technology friction and makes the adaptation of technology more human-centered.

The nature of a physician’s job may always put them at risk of burnout, but optimal use and consistent management of technology can make a positive impact. In healthcare, seeking technological solutions that reduce the burden of repetitive work—and then mapping the associated benefits and studying the effects on staff well-being and clinician resilience—provides deep insights.

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Role of Artificial Intelligence and Machine Learning in Speech Recognition

By The Signal

If you have ever wondered how your smartphone can comprehend instructions like “Call Mom,” “Send a Message to Boss,” “Play the Latest Songs,” “Switch ON the AC,” then you are not alone. But how is this done? The one simple answer is Speech Recognition. Speech Recognition has gone through the roof in the recent 4-5 years and is making our lives more comfortable every day. 

Speech Recognition was first introduced by IBM in 1962 when it unveiled the first machine capable of converting human voice to text. Today, powered by the latest technologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning, speech recognition is touching new milestones. 

This latest technological advancement is being used across the globe by top companies to make their user’s experience efficient and smooth. Technologies like Amazon’s Alexa, Apple’s Siri, Google Assistant, Google Speech, Google Dictate, Facebook’s Oculus VR, and Microsoft’s Cortana are all examples of Speech Recognition. 

The expanding usage of speech-to-text technologies has also opened many new job domains, and students are wonderfully exploiting them. Many students are now joining courses like PGP in AI and Machine Learning after completing their graduation to improve their prospects. The high salary package of around INR 15 lakh for freshers is the 2nd biggest reason attracting students towards this, the biggest reason being the fantastic job role. 

Speech Recognition was a very niche domain before the advent of AI and ML, which has completely transformed it now. Before we understand how AI and ML made changes, let’s understand the nuances of what all these terminologies are. 

Artificial Intelligence 

Artificial Intelligence is the technology by which machines become capable of demonstrating intelligence like humans or animals. Initially, AI was only about memorizing data and producing results accordingly; however, now it is much more than that as machines perform various activities like Speech Recognition, Object Recognition, Translating Texts, and a lot more. 

Another latest addition to AI has been Deep Learning. With the help of Deep Learning, machines can process data and create patterns that help them make valuable decisions. This behavior of a machine through Deep Learning is similar to the behavior of a human brain. Deep Learning activities can be “Supervised,” “Semi-Supervised,” as well as “Unsupervised.” 

Machine Learning 

Machine Learning is a subdomain of AI which teaches machines to memorize past events and activities. Through ML, machines are trained to retain various data sets’ information and outputs and identify patterns in these decisions. It allows the machine to learn by itself without the help of any programming code. 

An example of Machine Learning is the e-Commerce websites suggesting products to you. The code, once written, allows machines to evolve on themselves and analyze user behavior and thus recommend products according to their preferences and past purchases. This involves Zero Human Interference and makes use of approaches like Artificial Neural Networks (ANN). 

Speech Recognition 

Speech Recognition is simply the activity of comprehending a user’s voice and converting that into text. It is chiefly of 3 types: 

  1. Automatic Speech Recognition (ASR) 
  2. Computer Speech Recognition (CSR) 
  3. Speech to Text (STT) 

Note: Speech Recognition and Voice Recognition are two different things. While the former comprehends a voice sample and converts it into a text sample, the sole purpose of the latter is to identify the voice and recognize to whom it belongs. Voice Recognition is often used for security and authenticity purposes. 

How Has AI and ML Affected the Future of Speech Recognition? 

The usage of Speech Recognition in our devices has grown considerably due to the developments in AI and ML technologies. Speech Recognition is now being used for tasks ranging from awakening your appliances and gadgets to monitoring your fitness, playing mood-booster songs, running queries on search engines, and even making phone calls. 

The global market for Speech Recognition, currently growing at a Cumulative Annual Growth Rate (CAGR) of 17.2%, is expected to breach the $25 billion mark by 2025. However, there were enormous challenges initially that have been tackled with the use of AI and ML now. 

When in its initial phase, some of the biggest challenges for Speech Recognition were Poor Voice Recording Devices, Huge Noise in the Voice Samples, Different Pitches in Speech of the Same User, etc. In addition to this, the changing dialects and grammatical factors like Homonyms were also a big challenge. 

With the help of AI programs capable of filtering sound, canceling noise, and identifying the meaning of words depending on the context, most of these challenges have been tackled. Today, Speech Recognition shows an efficiency of 95%, which stood at less than 20% around 30 years back from now. The only biggest challenge remaining now for programmers is making machines capable of understanding emotions and feelings and satisfactory progress in this part. 

The increasing efficiency in Speech Recognition is becoming an essential driving factor in its success, and top tech giants are leveraging these benefits. More than 20% of users searched on Google through Voice in 2016 only, and this number is expected to be far more prominent now. Businesses today are automating their services to make their operations efficient and introducing Speech Recognition facilities at the top of their to-do lists. 

Some of the key usages of Speech Recognition today are listed below. 

  • The most common use of Speech Recognition is to perform basic activities like Searching on Google, Setting Reminders, Scheduling Meetings, Playing Songs, Controlling Synced Devices, etc. 
  • Speech Recognition is now also being used in various financial transactions, with some banks and financial companies offering the feature of “Voice Transfer” to their users. 

Speech Recognition is no doubt one of the best innovations made by expanding technological developments. However, there is one thing to be noted if you are also planning to enter this sector. The domain is inter-mingled, and the mere knowledge provided by a Speech Recognition course won’t be enough for you to survive in this field. 

Therefore, it is essential that you also sharpen your skills in allied concepts like Data Science, Data Analytics, Machine Learning, Artificial Intelligence, Neural Networks, DevOps, and Deep Learning. So what are you waiting for now? Hurry up and join an online course in Speech Recognition now!

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10 top Artificial Intelligence (AI) trends in 2021

Stephanie Overby for The Enterprises Project

Pre-pandemic,  artificial intelligence was already poised for huge growth in 2020. Back in September 2019, IDC predicted that spending on AI technologies would grow more than two and a half times to $97.9 billion by 2023. Since then, COVID-19 has only increased the potential value of AI to the enterprise. According to McKinsey’s State of AI survey published in November 2020, half of respondents say their organizations have adopted AI in at least one function.

“As the grip of the pandemic continues to affect the ability of the enterprise to operate, AI in many guises will become increasingly important as businesses seek to understand their COVID- affected data sets and continue to automate day-to-day tasks,” says Wayne Butterfield, director of ISG Automation, a unit of global technology research and advisory firm ISG.

Also, IT operations faced a lot of challenges and stress in 2020 given all of the shifts toward work-from-home capabilities, and that will most likely continue in 2021. AI plays here, too: “With businesses more digitally connected than ever before,” says Dan Simion, vice president of AI and analytics at Capgemini North America, “AI can ensure that they stay operational.”

AI trends 2021: What’s happening in the enterprise

However, the focus of AI adoption will not be simply to improve the efficiency or effectiveness of operations. “There has been a visible shift towards leveraging AI to improve stakeholder experience owing to the pandemic,” says Alisha Mittal, practice director with management consultancy and research firm Everest Group.

The AI trends expected in 2021 that IT leaders should monitor include the following:

1. AI talent will remain tight

Talent supply is expected to be a key issue accompanying the accelerated adoption of AI going into 2021. “Enterprises have started realizing the importance of democratizing AI to address this persistent AI talent gap,” Mittal says.CIOs have worked to make data accessible to non-technical users: Now, make sure AI is usable by a wider set of users.

Just as CIOs have worked to make data accessible

Just as CIOs have worked to make data accessible to non-technical users, they will need to make sure AI is usable by a wider set of users. “Successful implementation of AI democratization requires focus on key aspects of data, technology, and learning strategy, supported by a decentralized governance model,” says Mittal. “Enterprises must also focus on contextualization, change management, and governance.”

2. AI fuels self-directed IT

In 2021, we will see more AI solutions that can detect and remediate common IT problems on their own, predicts Simion of CapGemini. “These solutions will self-correct and self-heal any malfunctions or issues in a proactive way, reducing the downtime of a system or critical application,” Simion says. “This will allow teams to allocate their resources to the complex and higher-priority projects they should be focusing on.”

3. AI structures unstructured data

In the year ahead, enterprises will leverage machine vision and natural language processing (NLP) to facilitate the structuring of unstructured data such as images or emails, says ISG’s Butterfield. The goal? To create data that robotic process automation (RPA) technology can more readily use to automate transactional activity in the enterprise.

“We have seen a rise in RPA, which is the fastest-growing area of software adoption in the last 24 months. But RPA has its limitations – predominantly in that it can only process structured data,” Butterfield explains. “Using AI to complete the complex task of understanding unstructured data and then provide a defined output such as a customer’s intention will enable RPA to complete the action.”

4. IT pushes AI at a larger scale

“In 2020, we continued to observe significant AI adoption within IT organizations,” says Simion. “In 2021, I expect organizations to start to see the benefits of executing their AI and ML models – not only getting them into production, but also pushing them to scale.” One of the advantages of AI is that it can achieve ROI in real time, Simion notes, so this could be the year many organizations see their AI efforts begin to pay off.

5. More AI becomes explainable

As compared to black-box AI, look for models to become more transparent. “There will be a bigger focus on explainability,” says Dave Lucas, senior director of product at customer data hub Tealium. “Being able to clearly articulate to a layperson how each individual characteristic or data point contributes to the end prediction or result of the model.” As more and more data regulations surface, AI trust will be pivotal. 

6. AIOps gets big

The complexity of IT systems has been exponentially increasing for the past several years. Forrester recently noted that vendors have responded with platform solutions that combine several once-siloed monitoring disciplines – such as infrastructure, application, and networking. As mentioned in our recent primer on the topic, an AIOps solution enables “IT operations and other teams to improve key processes, tasks, and decision-making through improved analysis of the volumes and categories of data coming its way.”

Forrester advises IT leaders to look for AIOps providers who can empower cross-team collaboration through data correlation, provide end-to-end digital experience, and integrate seamlessly into the whole IT operations management toolchain.

7. Augmented processes enter the picture

Data and AI are key to competitive advantage and will be part of a bigger strategy for process automation and innovation. “Within that strategy, data ecosystems are scalable, governed, lean, and provide timely data from heterogeneous sources, but at the same time need to provide playgrounds and adapt fast to foster innovation,” says Ana Maloberti, big data engineer with Globant. “Companies are going a step further in optimization with augmented processes, both within business and development.”

Augmented coding tools, which are Globant’s main focus, optimize software development processes using AI, aiming for benefits including improved collaboration and wider collective intelligence. “The main challenge in making the most of this technology is a cultural one,” Maloberti says. “Fostering a data-driven organizational mindset first and growing out of experimental stages of AI are needed to create a sustainable and robust delivery model.”

8. Voice- and language-driven intelligence takes off

The increase in remote working will drive greater adoption of NLP and automated speech recognition (ASR) capabilities, particularly in customer contact centers.

The increase in remote working will drive greater adoption of NLP and automated speech recognition (ASR) capabilities, particularly in customer contact centers, predicts ISG’s Butterfield. “Historically, less than five percent of all customer contacts are routinely checked for quality and agent feedback,” Butterfield says. “With a lack of one-to-one coaching at the moment of support – a given in an office environment ­­– enterprises will need to use AI to complete checks on agent quality, customer intent understanding, and to ensure continued compliance.”

9. AI and cloud become symbiotic

“Artificial intelligence is going to play a significant part in broader adoption of cloud solutions,” says Rico Burnett, director of client innovation at legal services provider Exigent. “The monitoring and management of cloud resources and the vast amounts of data that will be generated will be supercharged through the deployment of artificial intelligence.”

10. AI ethics and standards come into focus

“In 2020, international partnerships like global Partnership on AI have moved from ideas to reality,” says Natalie Cartwright, co-founder and COO of AI banking platform Finn AI. “In 2021, they will deliver expertise and alignment on how to ensure that we leverage AI against major global problems, ensure inclusion and diversity, and stimulate innovation and economic growth.” Algorithm fairness and transparency of data are just two of the issues in the spotlight as AI ethics becomes more important to organizations across industries and society as a whole.

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Mayo Clinic CIO says AI has been key to understanding COVID-19

By Mike Miliard for Healthcare IT News

In his opening keynote Tuesday at the HIMSS Machine Learning & AI for Healthcare Digital Summit, Mayo Clinic CIO Cris Ross enumerated some of the many ways artificial intelligence has been crucial to our evolving understanding of COVID-19.

Way back in March, for instance, researchers were already using an AI algorithm – trained on data from the 2003 SARS outbreak – for “a recurrent neural network to predict numbers of new infections over time,” he said. “Even from the beginning of COVID-19, artificial intelligence is one of the tools that scientists have been using to try and respond to this urgent situation.”

And just this past month, Boston-based nference – whose clinical-analytics platform is used by Mayo Clinic – sifted through genetic data from 10,967 samples of novel coronavirus. Along the way, researchers discovered “a snippet of DNA code – a particular one that was distinct from predecessor coronaviruses,” said Ross. “The effect of that sequence was it mimics a protein that helps the human body regulate salt and fluid balances.

“That wasn’t something that they went looking for,” he said. “They simply discovered it in a large dataset. It’s since been replicated and used to support other research to discover how genetic mutations and other factors are present in COVID-19 that help, both with the diagnosis of the disease, but also its treatment.”

Many other now commonly understood characteristics of the novel coronavirus – the loss of smell it can cause, its effects on blood coagulation – were discovered using AI.

Around the world, algorithms are being put to work to “find powerful things that help us diagnose, manage and treat this disease, to watch its spread, to understand where it’s coming next, to understand the characteristics around the disease and to develop new therapies,” said Ross. “It’s certainly being used in things like vaccine development.”

At the same time, there are already some signs that “we need to be careful around how AI is used,” he said.

For example, the risk of algorithmic bias is very real.

“We know that Black and Hispanic patients are infected and die at higher rates than other populations. So we need to be vigilant for the possibility that that fact about the genetic or other predisposition that might be present in those populations could cause us to develop triage algorithms that might cause us to reduce resources available to Black or Hispanic patients because of one of the biases introduced by algorithm development.”

The profusion of data since the pandemic began has allowed advanced models to be purpose-built at speed – and has also enabled surprise findings along the way.

Sure, “some of the models that are being built that are labeled AI are really just fancy regression models,” said Ross. “But in a way, does it really matter? In any case, [they’re] ways to use data in powerful ways to discover new things, … drive new insights, and to bring advantages to all of us dealing with this disease.”

It’s notable too that the big datasets needed for AI and machine learning “simply didn’t exist in the pre-electronic health record days,” he added.

“Just imagine where we would have been if it was a decade ago and we were trying to battle COVID-19 with data that had to be abstracted from paper files, … manila folders, and a medical records room someplace,” said Ross.

“The investments we’ve made to digitize healthcare have paid off. We’ve learned that the downstream value of data that’s contained in electronic health records systems is incredibly powerful.”

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Leveraging The Power Of AI In Telehealth

By Sindhu Kutty for Forbes

As providers move toward increasing virtual care options across the care continuum, the use of AI in telehealth to enable physicians to make real-time, rich, data-driven decisions is a major factor in creating a better patient journey and better health outcomes. According to a study by MIT, 75% of healthcare institutions that implemented AI acknowledged an augmented ability to treat illnesses, and 4 in 5 said it proactively helped avert workplace burnout. With Covid-19 putting an increasing strain on both areas (volume of clinical information and associated patients and the increased workload for clinicians), AI in telehealth is a powerful approach for the future of care delivery.

I advise hospital executives on healthcare and health IT strategies, and the top two concerns I hear around AI are the perception that AI can replace physicians (it can’t) and the complexity of utilizing consumer-generated data in preparation of either virtual or in-person visits (for example, data from smartwatches that gather blood pressure and heart rate information). One of the primary goals for effective patient care is preventing hospitalization and the associated healthcare costs, so proactive implementation of treatment options is top of mind.

So, what are some ways in which AI and telehealth can partner to achieve these outcomes?

AI can augment physicians in making medical decisions (and I emphasize augment, not replace, here intentionally). For example, instead of a physician relying on two or three pieces of medical information (such as medical history, an exam and a lab test), AI can scour big datasets across thousands of patients with similar illness profiles from monitoring devices or medical telemetry products (including treatment protocols, side effects, etc.) to produce algorithmic patterns that can suggest potential next steps to the physician. The physician can then leverage this information to enhance their ability to treat illnesses.

Another way that AI can assist is with clinical information access. With the rise in synchronous (live, two-way audiovisual interactions) telehealth sessions due to Covid-19, physicians have faced several challenges. For example, accessing lab results and other EHR content while on a virtual call with patients was an administrative nightmare for providers both during the session (with the need to access different systems for clinical information) and after (recording the visit and treatment provided). So how can we equip the provider better while not compromising the patient’s experience? Several platforms can push real-time clinical information into virtual platforms using a configuration tool for seamless integration. These platforms typically have an AI aspect, which can deliver relevant data from disparate EHR systems across multiple care settings (for example, labs, X-rays) on a real-time basis. AI can also assist providers by making administrative tasks relating to medical records less onerous through voice-controlled tools based on natural language processing technology. The visit note can be automatically captured and transcribed into the electronic health record.

I mentioned consumer-generated data being a big area in terms of potential for proactive, early risk reduction. Telehealth applications can allow clinicians to monitor ECG, heartbeat, blood pressure, temperature and other vital signs remotely. This combined with AI can allow the patient and physician to be alerted about potential health conditions that are tailored to the unique patient’s needs through predictive analytics and setting monitoring thresholds. This combination can enhance the quality of care provided, the patient’s experiences and health outcomes, and the physicians’ experiences (less data fatigue and more proactive information).

Technology advances in AI, such as voice recognition, have led to the emergence of chatbots or conversational agents. Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa and Google Home have made this a household technology. Motivational messages, appointment or health reminders, health information and symptom checking, and assistance for the elderly in their homes are all healthcare use cases that are in play today. Conversational agents today offer asynchronous interactions that supplement traditional care delivery. As the comfort with this technology increases and patient safety concerns are addressed, the complexity of the use cases linked to medical history can be explored further to extend care delivery versus just supplementing it.

The implementation of AI in telehealth settings is gaining momentum. The NextDREAM Consortium Group conducted a study about the efficacy of AI in remote diabetes care. The primary finding was that consistent insulin adjustments transmitted remotely, which use the trialed automated AI system, can be as effective as expert physician dose adjustments. The automated AI-based system can be used by physicians and specialists for decision support. The University of San Francisco’s Center for Digital Health Innovation is testing AI that can read X-rays as an early warning detection system for pneumothorax.

The main AI use cases in telehealth include information analysis and collaboration, remote patient monitoring, and intelligent diagnostics and assistance. The power of AI can be leveraged to augment physicians in their abilities to diagnose and treat patients, mitigate their own burnout and enhance the entire spectrum of the patient journey. Due to the ongoing public health crisis, the focus on AI and telehealth continues to be a strong push for healthcare executives as they look to stay competitive by streamlining clinicians’ workflows and unlocking predictive potential through the analysis of patient data.

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Why Voice Tech Will Be the Post-Crisis Standard — and Not Just for Ordering Pizza

Shafin Tejani for Entrepreneur

My kids, ages 8 and 5, are showing me the future. When I want to watch a movie or turn out the lights, I instinctively reach for the remote or flick a switch. My children find it far more natural to just ask Siri for Peppa Pig, or tell Alexa to darken the room. Tapping a keyboard or clicking a mouse? Lame and old-fashioned. Why not just talk to the machines around us like we talk to each other?

Of course, right now talking — rather than touching — also has serious safety upsides. Voice tech adoption has accelerated as the coronavirus pandemic makes everyone touchy about how sanitary it is to poke buttons and screens. But the reality is the 2020s were poised to be the decade of voice technology well before the crisis hit.  

Indeed, thanks to a convergence of technology, necessity and demographic shifts, voice is in the unique position to become not just increasingly popular but the dominant user interface going forward. Before long, we’ll all be conversing with our devices pretty much non-stop, and to do much more than just set timers and fetch weather reports. 

And much like the desktop software industry back in the day and smartphone apps after that, a multibillion-dollar business ecosystem is about to surge around voice tech — at least, for entrepreneurs and businesses ready to ride the voice wave.   

How voice tech went from talk to action

Getting to the point where we can casually ask our Apple Watches for nearby dinner recommendations is no small feat. It required the integration of decades of advancements in AI-driven natural-language-processing, speech recognition, computing horsepower, and wireless networking, to name just a few building blocks. 

And yet, we’re just starting to grasp the potential of these technologies. Voice is the ultimate user interface because it’s not really a UI, but part of what we are as humans and how we communicate. There’s almost no learning curve required like there is when people take typing classes. Voice-enabled machines learn to adapt to our natural behaviors rather than the other way around. My kids love joking with Siri — nobody clowns around with a keyboard.

The business model around voice tech is crystallizing, as well. Developing AI and related technologies is complex and costly, so mega-capitalized giants like Google, Apple and Amazon have built an insurmountable first-mover advantage and dug a moat behind them. But they’ve also created countless lucrative niches in their ecosystems for other companies. 

Just as the iPhone gave birth to a $6.3 trillion dollar mobile app economy, platforms like Alexa and Google Assistant have already created opportunities for developers to create more than 100,000 Alexa “skills” and 4000 Google Assistant apps or actions. In the years ahead, that ecosystem will likely grow to rival traditional apps in number and value.  

The coronavirus pandemic is further boosting the adoption of voice-enabled technology, with 36% of U.S. smart-speaker owners reporting they’ve increased their use of their devices for news and information. And hygienic concerns are bringing contactless technologies like voice-controlled elevators out of the realm of fiction (and sketch comedies) and into offices and public spaces, so people don’t have to touch the same buttons and keypads as countless strangers.

How voice can take us “back to the future” in terms of human interaction

Yet for all the advances we’ve achieved, we’re still in the Voice 1.0 era. We’re mostly just commanding our devices to execute simple tasks, like setting alarms or telling us sports scores. In reality, this is just the beginning of what’s possible.

Machine learning underpins voice technology, and the AI gets smarter as we feed it more data. The number of voice-enabled devices in use is soaring — sales of smart speakers increased by 70% between 2018 and 2019 — flooding computers with more data to learn from. And that doesn’t count the billions of smartphone users talking to Siri and Google Assistant. Machines are growing much smarter, much faster.

Amazon and Google may soon take machines’ conversational skills to a deeper level. Both companies have filed patents for technology to read emotions in people’s voices. Marketers might salivate over the prospect of advertising products that suit how customers are feeling at the moment (“You sound hangry — how about a takeout pizza?”), but the applications for emotionally attuned bots don’t have to be so crassly commercial.

Spike Jonze’s movie Her, for example, tells the story of a lonely writer who develops a passionate relationship with his computer operating system, Samantha, as Samantha learns to become more conscious, self-aware and emotionally intelligent.

Robotic companionship seemed far-fetched when the film came out in 2013, but when this year’s pandemic locked millions down into isolation, hundreds of thousands downloaded Replika, a chatbot phone app that provides friendship and human-like conversation. People can develop genuine attachment to conversant machines, as seniors do with Zora, a human-controlled robot caregiver.

Why the booming voice market is just beginning

Coming months and years will see not only improved tech, but an expansion of voice to nearly all areas of business and life. Ultimately, voice technology isn’t a single industry, after all. Rather, it’s a transformative technology that disrupts nearly every industry, like smartphones and the internet did before. The voice and speech recognition market is expected to grow at a 17.2% compound annualized rate to reach $26.8 billion by 2025. Meanwhile, AI — the technology that underpins voice, and in many respects parallels its true potential — is estimated to add $5.8 trillion in value annually.

But unlike other technological advances that have radically changed how we live, voice technologies promise to make machines and people alike behave more like humans. In terms of adoption rates, applications and market, the possibilities are enough to leave one, well, speechless.