Israeli robotics startup Intuition Robotics has raised $36 million in a series B round of funding co-led by Sparx Group and OurCrowd, with participation from Samsung Next, Toyota AI Ventures, Bloomberg Beta, iRobot, Sompo Holdings, Union Tech Ventures, Happiness Capital, and Capital Point.
Founded in 2015, Intuition Robotics is creating what it calls “social companion” robots and related technologies, with an initial focus on reducing loneliness and isolation in elderly people. The company’s first product was a $1,500 robot called ElliQ that opened for preorders last January and has accumulated “over 10,000 days” in homes across the U.S., though the company hasn’t revealed specific sales figures. The majority of ElliQ’s users are between 80 and 90 years of age.
ElliQ more closely resembles a desk lamp than a humanoid, and it sits on a small dock with a tablet screen and cameras. It can communicate using natural language conversation and can also employ other methods to convey a message or emotion — including shifting its position and using sound and light to express itself. It also promises to improve over time as it adapts to its owner.
Above: ElliQ from Intuition Robotics
Unlike Alexa or Google Assistant, ElliQ is designed to proactively engage with users, rather than passively waiting for a prompt. It will detect if its owner is currently doing something — like having a conversation or watching TV — and leave them be. But if it suspects the person is sitting idle, it may ask them a question or suggest that they get up and go for a walk. It could also play music it knows the user likes, or tell them a joke.
Above: ElliQ is proactive and context-aware
Digital assistants to digital companions
Ultimately, Intuition Robotics sees its platform as facilitating the evolution from digital assistants to digital companions that fully understand context and can make decisions based on preset goals. ElliQ is just a demonstration of the underlying technology — last year, Intuition Robotics launched a cognitive AI platform called Q that brings the technology to third-party devices.
The company has built its cognitive AI technology entire in-house and claims that it’s protected by “dozens of patent applications.” But its customers — and the in-house team working on ElliQ — do lean on some off-the-shelf products for sensor and perception needs, such as automatic speech recognition (ASR) and text-to-speech.
The company’s first Q platform customer was an existing investor, the Toyota Research Institute (TRI), which is developing an in-car digital companion. The idea here is that drivers and passengers will be able to engage with an AI-powered agent that is proactive and context-aware. No timescale has been given for when this collaboration might yield fruit.
“We are making fast progress, and we hope Toyota will release information soon,” Intuition Robotics cofounder and CEO Dor Skuler told VentureBeat.
The company had previously raised $22 million, including its $14 million series A round from 2017 and a follow-on $6 million tranche from Samsung Next a year later. With another $36 million in the bank, Intuition Robotics said it’s now looking to expand its Q platform into other industries and use cases, though it said it’s too early to supply specific examples of additional partnerships.
“Think of use cases where a relationship would make sense and add value to a user,” Skuler added. “This can be interaction with smart devices or robots, education, hospitality, home appliances, and much more. This investment will fuel the evolution of agents from utilitarian digital assistants to full-fledged digital companions that are at our side, anticipating our needs and seamlessly, proactively improving our lives by helping us achieve certain outcomes.”
Intuition Robotics claims around 85 employees — including machine learning and computer vision experts — spread across its native Israel, in addition to hubs in San Francisco and Greece.
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When my son was a toddler, he went to his pediatrician for a routine CAT scan. Easy stuff. Just a little shot to subdue him for a few minutes. He’d be awake and finished in a jiffy.
Except my son didn’t wake up. He lay there on the clinic table, unresponsive, his vitals slowly falling. The clinic had no ability to diagnose his condition. Five minutes later, he was in the back of an ambulance. My wife and I were powerless to do anything but look on, frantic with worry for our boy’s life.
It turned out that he’d had a bad reaction to a common hydrochloride sedative. Once that was figured out, doctors quickly brought him back around, and he was fine.
What if…
But what if, through groundbreaking mixtures of compute, database, and AI technologies, a quick round of analyses on his blood and genome could have revealed his potential for such a reaction before it became a critical issue?
What if it were possible to devise a course of treatment specific to him and his body’s unique conditions, rather than accepting a cookie-cutter approach and dealing with the ill effects immediately after?
And what if that could be done with small, even portable medical devices equipped with high-bandwidth connectivity to larger resources?
In short, what if, through the power of superior computing and next-generation wireless connectivity, millions of people like my son could be quickly, accurately treated on-site rather than endure the cost and trauma of legacy medical methods?
Above: Pinpointing diagnosis and treatment that’s right for you.
These questions I asked about my son are at the heart of today’s efforts in precision medicine. It’s the practice of crafting treatments tailored to individuals based on their characteristics. Precision medicine spans an increasing number of fields, including oncology, immunology, psychiatry, and respiratory disorders, and its back end is filled with big data analytics.
Key Points:
Precision medicine uses a patient’s individual characteristics, including genetics, to identify highly specific, optimized healthcare steps.
5G and new generations of wireless and processors are needed to provide the speed and accessibility required.
Optimizing workloads for parallelized processing makes precision medicine more practical.
Visions like Intel’s “All in One Day” use AI, 5G, and medical IoT to take a patient from examination to precision treatment in 24 hours.
Data drives individual-centric care
Pairing drugs to gene characteristics only covers a fraction of the types of data that can be pooled to target specific patient care.
Consider the Montefiore Health System in the Bronx. It has deployed a semantic data lake, an architecture for collecting large, disparate volumes of data and collating them into usable forms with the help of AI. Besides the wide range of data specific to patients collected onsite (including from a host of medical sensors and devices), Montefiore healthcare professionals also collate data from sources as needed, including PharmGKB databank (genetic variations and drug responses), the National Institute of Health’s Unified Medical Language System (UMLS), and the Online Mendelian Inheritance in Man (human genomic data).
Long story short, the Intel/Cloudera/Franz-based solution proved able to accurately create risk scores for patients, predict whether they would have a critical respiratory event, and advise doctors on what actions to take.
Above: The semantic data lake architecture implemented by Montefiore Health System pulls from multiple databases to address open-ended queries and provide a range of actionable healthcare results.
“We are using information for the most critically ill patients in the institution to try and identify those at risk of developing respiratory failure (so) we can change the trajectory,” noted Dr. Andrew Racine, Montefiore’s system SVP and chief medical officer.
Now that institutions like Montefiore can perform AI-driven analytics across many databases, the next step may be to integrate off-site communications via 5G networking. Doing so will enable physicians to contribute data from the field, from emergency sites to routine in-home visits, and receive real-time advice on how to proceed. Not only can this enable healthcare professionals to deliver faster, more accurate diagnoses, it may permit general physicians to offer specialized advice tailored to a specific patient’s individual needs. Enabling caregivers like this with guidance from afar is critical in a world that, researchers say, faces a shortage 15 million healthcare workers by 2030.
What it will take. (AI, for starters)
Enabling services like these is not trivial — in any way. Consider the millions of people who might need to be genetically sequenced in order to arrive at a broad enough sample population for such diagnostics. That’s only the beginning. Different databases must be combined, often over immense distances via the cloud, without sacrificing patients’ rights or privacy. Despite the clear need for this, according to the Wall Street Journal, only 4% of U.S. cancer patients in clinical trials have their genomic data made available for research, leaving most treatment outcomes unknown to the research and diagnostic communities. New methods of preserving patient anonymity and data security across systems and databases should go a long way toward remedying this.
One promising example: using the processing efficiencies of Intel Xeon platforms in handling the transparent data encryption (TDE) of Epic EHR patient information with Oracle Database. Advocates say the more encryption and trusted execution technologies, such as SGX, can be integrated from medical edge devices to core data centers, the more the public will learn to allow its data to be collected and used.
Beyond security, precision medicine demands exceptional compute power. Molecular modeling and simulations must be run to assess how a drug interacts with particular patient groups, and then perhaps run again to see how that drug performs the same actions in the presence of other drugs. Such testing is why it can take billions of dollars and over a decade to bring a single drug to market.
Fortunately, many groups are employing new technologies to radically accelerate this process. Artificial intelligence plays a key role in accelerating and improving the repetitive, rote operations involved in many healthcare and life sciences tasks.
Pharmaceuticals titan Novartis, for example, uses deep neural network (DNN) technology to accelerate high-content screening, which is the analysis of cellular-level images to determine how they would react when exposed to varying genetic or chemical interactions. By updating the processing platform to the latest Xeon generation, parallelizing the workload, and using tools like the Intel Data Analytics Acceleration Library (DAAL) and Intel Caffe, Novartis realized nearly a 22x performance improvement compared to the prior configuration. These are the sorts of benefits healthcare organizations can expect from updating legacy processes with platforms optimized for acceleration through AI and high levels of parallelization.
Faster than trained radiologists
Interestingly, such order-of-magnitude leaps in capability, while essential for taming the torrents of data flowing into medical databases, can also be applied to medical IoT devices. Think about X-ray machines. They’re basically cameras that require human specialists (radiologists) to review images and look for patterns of health or malady before passing findings to doctors. According to GE Healthcare, hospitals now generate 50 petabytes of data annually. A “staggering” 90% comes from medical imaging,” GE says, with more than 97% unanalyzed or unused. Beyond working to use AI to help reduce the massive volume of “reject” images, and thus cut reduce on multiple fronts, GE Healthcare teamed with Intel to create an X-ray system able to capture images and detect a collapsed lung (pneumothorax) within seconds.
Simply being able to detect pneumothorax incidents with AI represents a huge leap. However, part of the project’s objective was to deliver accurate results more quickly and so help to automate part of the diagnostic workload jamming up so many radiology departments. Intel helped to integrate its OpenVINO toolkit, which enables development of applications that emulate human vision and visual pattern recognition. Those workloads can then be adapted for processing across CPUs, GPU, AI-specific accelerators and other processors.
With the optimization, the GE X-ray system performed inferences (image assessments) 3.3x faster than without. Completion time was less than one second per image — dramatically faster than highly trained radiologists. And, as shown in the image above, GE’s Optima XR240amx X-ray system is portable. So this IoT device can deliver results from a wide range of places and send results directly to doctors’ devices in real time over fast connections, such as 5G. A future version could feed analyzed X-rays straight into patient records. There, they become another factor in the multivariate pool that constitutes the patient’s dataset, which in turn, enables personalized recommendations by doctors.
What we’re dealing with
By now, you see the problem/solution pattern:
Traditional medical practices are having trouble scaling across a growing, aging global population. Part of the problem stems from the medical industry generating far more data than its infrastructure can presently handle.
AI can help to automate many of the tasks performed by health specialists.
By applying AI to a range of medical data types and sources, care recommendations can be tailored to individual patients based on their specific characteristics for greater accuracy and efficacy rather than suggesting blanket practices more likely to yield unwanted outcomes.
AI can be accelerated through the use of hardware/software platforms designed specifically for those workloads.
AI-enabled platforms can be embedded within and connected to medical IoT devices, providing new levels of functionality and value.
IoT devices and their attached ecosystem can be equipped with connectivity such as 5G to extend their utility and value to those growing populations.
The U.S. provides a solid illustration of the impact of population in this progression. According to the U.S. Centers for Disease Control (CDC), even though the rate of new cancer incidents has flattened in the last several years, the country’s rising population pushed the number of new cases diagnosed from 1.5 million in 2010 to 1.9 million in 2020, driven in part by rising rates in overweight, obesity, and infections.
The white paper “Accelerating Clinical Genomics to Transform Cancer Care” (below) paints a stark picture of the durations involved in traditional approaches to handling new cancer cases from initial patient visit to data-driven treatment.
At each step, delays plague the process — extending patient anxiety, increasing pain, even leading to unnecessary death.
All in one day
Intel created an initiative called “All in One Day” to create a goal for the medical industry: take a patient from initial scan(s) to precision medicine-based actions for remediation in only 24 hours. This includes genetic sequencing, analysis that yields insights into the cellular- and molecular-level pathways involved in the cancer, and identification of gene-targeted drugs able to deliver the safest, most effective remedy possible.
To make All in One Day possible, the industry will require secure, broadly trusted methods for regularly exchanging petabytes of data. (Intel notes that a typical genetic sequence creates roughly 1TB of data. Now, multiply that across the thousands of genome sequences involved in many genomic analysis operations.) The crunching of these giant data sets calls for AI and computational horsepower beyond what today’s massively parallel accelerators can do. But the performance is coming.
As doctors will have to service ever-larger patient populations, expect them to need data results and visualizations delivered to wherever they may be, including in forms animated or rendered in virtual reality. This will require 5G-type wireless connectivity to facilitate sufficient data bandwidth to whatever medical IoT devices are being used.
If successful, more people will get more personalized help and relief than ever possible. The medical IoT and 5G dovetail with other trends now reshaping modern medicine and making these visions everyday reality. A 2018 Intel survey showed that 37% of healthcare industry respondents already use AI; the number should rise to 54% by 2023. Promising new products and approaches appear daily. A few recent examples are here, here and here.
As AI adoption continues and pairs with faster hardware, more diverse medical devices, and faster connectivity, perhaps we will soon reach a time when no parent ever has to watch an unresponsive child whisked away by ambulance because of adverse reactions that might have been avoided through precision medicine and next-gen technology.
This article is part of the Technology Insight series, made possible with funding from Intel.
Wearable health monitors, ubiquitous sensors, and the ability to collect and store huge amounts of data are creating challenges for researchers hoping to use artificial intelligence to identify diseases. While the gathered data can hold important clinical answers, finding those answers means that the data must be categorized and labeled.
Now, researchers at MIT have developed a system that can autonomously identify signs of a disease from data gathered from a relatively small group of people and without any initial training.
The research, recently presented at the Machine Learning for Healthcare conference in Ann Arbor, Michigan, focused on learning the audio biomarkers of vocal cord disorders. Using data gathered over a week from an accelerometer attached to the necks of 100 people, the system automatically identified which sound characteristics were important for identifying whether a patient has vocal cord nodules.
“It’s becoming increasing easy to collect long time-series datasets. But you have physicians that need to apply their knowledge to labeling the dataset,” said lead author Jose Javier Gonzalez Ortiz, a PhD student at MIT. “We want to remove that manual part for the experts and offload all feature engineering to a machine-learning model.”
While the system was utilized for a specific sound-related task, it can be trained to analyze data from other diseases. The current study may help to create tools that prevent vocal nodules and help to study the onset of this condition.
Alphabet CEO Sundar Pichai said artificial intelligence is “more profound than fire or electricity.” Author and historian Yuval Noah Harari said, “If you have enough data about me, enough computing power and biological knowledge, you can hack my body, my brain, my life, and you can understand me better than I understand myself.” And a recent Brookings Institution report prophesied that the country or region leading in AI in 2030 will rule the planet until at least 2100.
It’s clear the AI revolution will impact society drastically, both for good and bad. Recently, using natural-language processing and machine-learning techniques, Canadian company BluDot sifted through global news reports, airline data, and reports of animal disease outbreaks to issue an alert about the current Coronavirus outbreak, days ahead of official organizations such as the World Health Organization. But an AI survey last year found about 70% of the public and technology leaders believe AI will lead to greater social isolation and a loss of human intellect and creativity. Already, data collected about us without our knowledge and processed by AI applications manipulate our wants and beliefs, effectively controlling people for commercial or political purposes. Deepfake videos created with readily available AI tools pose an additional challenge, further undermining our ability to know what is real and what is fake. And, of course, AI is expected to take many of our jobs.
Governments are at cross purposes
Given the force of this technology, shouldn’t governments be bracing for its effect with robust regulations? The U.S. government so far is taking a mostly hands-off approach. U.S. Chief Technology Officer Michael Kratsios warned federal agencies against over-regulating companies developing artificial intelligence. There are views, too, that the U.S. government doesn’t want to issue meaningful regulation, that the administration finds regulation antithetical to its core beliefs.
There is greater movement underway by the European Union (EU), which will issue a paper in February proposing new AI regulations for “high-risk sectors,” such as healthcare and transport. These rules could inhibit AI innovation in the EU, but officials say they want to harmonize and streamline rules in the region. China is pursuing a different strategy designed to tilt the playing field to its advantage as exemplified by its standards efforts for facial recognition. Ultimately, it is in the worldwide public interest for the AI superpowers, the U.S. and China, to collaborate on common AI principles. But at present it appears there is no ongoing dialog, effectively creating a regulatory standoff.
Perhaps that is okay, as the just released 2020 Edelman Trust Barometer highlights a lack of faith from the public in the ability of government to understand how to regulate AI and other emerging technologies.
Above: Source: 2020 Edelman Trust Barometer
This governance lag is due both to conflicting national and regional interests and to the high velocity with which AI technologies have progressed. These twin drivers are at the crux of the challenge for how to regulate this revolution. Government may try but is largely unequipped to create and enforce meaningful regulation for public benefit. That leaves private industry to foster regulation. But will they do it?
Big Tech wants a light touch
Those leading the AI revolution are the giant technology companies – what Amy Webb, the Founder of the Future Today Institute, refers to as “The Big Nine.” These include U.S. firms Google, Microsoft, Amazon, Facebook, IBM, and Apple plus China’s Baidu, Alibaba, and Tencent. Arguably, it’s not only the future of AI that they control but possibly the future of humanity. Taken together, they form a global oligopoly with near total control of the technology and its underlying data. While there is growing consensus among them on the need for some form of regulation, they’ve voiced different views on how to go about it.
At Davos this year. Ginni Rometty, now the former IBM CEO, called for “precision regulation,” to allow for AI technical advancement and the ability to compete in a global marketplace. This view is similar to one espoused by Tom Wheeler of the Brookings Center for Innovation. He says regulation should focus on the technology’s effects rather than chase broad-based fears about the technology itself. They are both basically saying that companies should have the freedom to create leading technologies without regulations slowing them down.
Alphabet’s Pichai recently advised the European Commission to take a “proportionate approach” when drafting AI rules and followed this with an op-ed in which he argued that AI technology needs to be harnessed for good and available to everyone. Microsoft president Brad Smith said the world “should not wait for the technology to mature” before regulating AI.
While it’s possible the Big Nine companies have the public’s interest at heart when they propose regulation strategies, it’s equally plausible that they see regulations as a way to increase barriers to entry into the AI space, further cementing their leadership positions while fending-off potential anti-trust lawsuits.
Speeches at Davos and op-eds are all well and fine and perhaps heartfelt, but real actions point to the conflicting interests of these companies. Mostly they support regulation that is light on specifics, and they often oppose more encompassing (or what they see as restrictive) efforts. The regulations that emerge over the next several years are likely to be a patchwork, varying by geography and degrees of specificity, open to interpretation and with enough loopholes to be largely ineffective.
The public is just trying to adapt
This gives rise to a larger question about whether the AI revolution can be effectively regulated at all. In the meantime, Harvard professor Shoshana Zuboff said in a recent op-ed: “Without law, we scramble to hide in our own lives, while our children debate encryption strategies around the dinner table and students wear masks to public protests as protection from facial recognition systems built with family photos.”
The AI revolution is underway. Conventional wisdom is that people are ultimately in control. There may still be time and opportunity for meaningful worldwide regulation, however the conflicting views between governments make this unlikely, and vast industry lobbying will strive to keep rules effectively weak. In a fast-moving revolution, however, the notion of control may only be an illusion.
Gary Grossman is the Senior VP of Technology Practice at Edelman and Global Lead of the Edelman AI Center of Excellence.
Cardiologs, a French medical technology startup that’s leveraging artificial intelligence (AI) to help detect heart conditions, has raised $15 million in series A funding. The round was led by Paris-based venture capital (VC) firm Alven, which touts its credentials for helping French-founded startups expand into the U.S. Other participants include Bpifrance, Idinvest Partners, Kurma Diagnostics, ISAI, and Paris Saclay Seed Fund.
Founded in 2014, Cardiologs has worked with physicians and cardiologists since its inception to develop a database of around 1.5 million electrocardiography (ECG) recordings, with deep learning algorithms leveraged to recognize patterns and diagnose heart conditions in patients more quickly. Readings garnered from Holter monitors, or ECG devices for tracking heart activity, are fed into Cardiologs’ system, which then displays analysis of the recordings. Cardiologs’ interface also highlights specific “episodes” for the cardiologist to check.
Cardiologs is ultimately what’s known as a clinical decision support system (CDSS), and it’s designed to help physicians detect anomalies in a patient’s heart activity.
Above: Cardiologs interface
Cardiologs is mostly targeted at helping physicians screen for an abnormal heart rhythm known as atrial fibrillation (AFib). AFib is thought to affect around 33 million people globally and is associated with an increased risk of severe stroke and heart failure. However, Cardiologs said it’s capable of spotting more than 100 different kinds of cardiac abnormalities.
“We have developed a new category of heart disease diagnostic products powered by AI that promise to revolutionize health care by delivering accurate, cost-effective, and timely expert-level diagnostics,” said Cardiologs CEO and cofounder Yann Fleureau.
The startup gained regulatory clearance in Europe back in 2016 with one of the first deep learning-powered medical devices to achieve CE status and later attained similar certification with the U.S. Food and Drug Administration.
AI in health
AI is infiltrating just about every industry, and the medical sphere is no different — it’s all about making sense of vast swathes of historical data, which is where machine learning comes into play. California-based Eko, which is working along similar lines as Cardiologs, raised $20 million just a few months back, while London’s Kheiron Technologies closed $22 million in funding for AI that helps radiologists detect cancer earlier.
Cardiologs had previously raised around $10 million, including a $6.4 million series A round back in 2017, and with another $15 million in the bank it plans to double down on growth in North America and Europe. The startup said it also plans to develop new integrations and applications for its technology.
“With its unique software built around a cutting-edge technology that blends deep learning with diagnostic clinical science and workflow, Cardiologs is already improving a traditionally manually processed industry to generate substantial improvements in the speed, cost, and accuracy of diagnostics,” added Alven partner François Meteyer. “This will be a key differentiator to build a new AI-based category in the cardiology field, democratizing the access to instant, reliable, and affordable expertise for every patient, every test, everywhere.”
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The adoption of artificial intelligence in healthcare has been a hot topic and rightly so. AI innovation has already demonstrated significant promise in healthcare by reducing costs to providers and improving quality and access to patients.
Accenture predicts the healthcare AI market to be worth $6.6 billion by 2021 and experience a 40% CAGR. By the same token, the survey results of 200 healthcare decision makers conducted by Intel and Convergys Analytics indicated that 54% expect to see the widespread adoption of AI within the next 5 years. Of the same group of executive respondents, 83% agreed that AI will provide a competitive advantage.
Simply put, the time is now for artificial intelligence in healthcare.
While there are many powerful use cases of AI in healthcare, challenges still remain. At a high level, the key to successful AI adoption requires people, processes, and technology to work in harmony. Ensuring patients’ trust, upskilling talent, having a clearly defined digital strategy, along with ways to measure ROI are among top concerns that hinder successful AI adoption. Consider this your roadmap to overcoming the barriers of AI adoption in your organization.
Problem: Patients don’t trust artificial intelligence in healthcare.
Artificial Intelligence has disrupted multiple industries from marketing to financial services, to supply chain management. In fact, AI innovation is so embedded in our daily lives sometimes we don’t even notice it. While it may be perfectly harmless to have an AI algorithm make recommendations for what to watch next on Netflix, trusting technology to provide accurate health recommendations has far greater implications.
Ninety-one percent of healthcare decision makers surveyed by Intel and Convergys Analytics recognized the benefits of AI but 54% of them fear AI will be responsible for a fatal error. There have been numerous cases where AI has been less than perfect. The instances of Google and Microsoft’s AI going wrong were embarrassing while the accidents involving the self-driving Uber or Tesla were fatal. Taken together, it would make sense why patients would want an opinion from a human expert over that of a machine–even when they’re wrong. As the age old adage in medical ethics states, “First do no harm,” healthcare leaders are erring on the side of caution when considering AI adoption.
Specific to healthcare innovation, consider IBM’s Watson for Oncology, an AI-powered supercomputer that promises to revolutionize the treatment of 12 cancers accounting for 80% of the world’s cases. According to a STAT investigation of the technology, Watson for Oncology has not lived up to its claims. Three years after IBM started selling this technology, STAT found that the supercomputer was still struggling differentiate between specific forms of cancer and received complaints from doctors outside of the US that treatment recommendations were biased toward American patients.
While IBM marketed Watson for Oncology as a cancer care product, there was a total lack of scientific publications showing how the technology changed doctors’ and patients’ experiences. To add to patients’ mistrust and confusion, when the machine offered a treatment recommendation, it couldn’t explain why the recommended course of treatment was credible because the machine learning algorithms were too complex for the average user to understand.
Solution: Be transparent. Inform patients about the benefits of AI in healthcare innovation and how it works.
In order to ensure patients’ trust in AI innovation, be transparent about the benefits of the technology and how it works. Unless the patient is an expert, AI and machine learning are inherently complex; help patients understand the benefits of AI and be clear about how the technology can support their care. When lives and treatment outcomes are at stake, it is imperative to ensure transparency for providers and patients.
Per the guidelines of the Clinical Decision Support (CDS) Coalition, developers and vendors of clinical support decision tools, especially those powered by machine learning must be transparent about what the product can and can’t do, its data sources, and potential drawbacks if providers were to use it. By educating healthcare providers on how a tool generates recommendations, it helps protect patients and earns trust.
Problem: Employees worry about Artificial Intelligence jeopardizing job security.
According to a 2018 survey by MindEdge of 1,000 managers across multiple industries, 42% of them believe that AI automation and robotics will eliminate jobs. In addition, 40% of these leaders said their employees lacked the skills needed for AI adoption.
AI automation and robotics will cause a shift in skills needed in the workforce. The results of a 2018 McKinsey study indicate that there will be an increased demand for technological, higher cognitive skills, and social, and emotional skills as widespread AI adoption continues. Specific to artificial intelligence in healthcare, 21% of workers were concerned about their job security due to AI automation and robotics within the next 12 months.
Solution: Foster a digital culture and augment your staff with innovation.
Consider your employees the foundation on which your AI transformation will be built and foster a digital culture. Engage your staff by adopting the trademarks of digitally mature organizations –the willingness to take risks, experiment, and invest heavily in upskilling talent. According to a study by the Boston Consulting Group, 9 out of 10 successful digital transformations focused on culture.
According to Infosys, the most successful enterprises recognize employees are key to driving successful AI adoption. Also, the most forward thinking executives believe that AI innovation will create more opportunities for employees rather than eliminate them.
Consider the idea of augmented intelligence, instead of deploying AI to replace your staff, consider using it to “amplify their capabilities.” It’s not a question of innovation or humans, its about combining the two to form a better long term solution. To bolster this notion, Amazon CEO, Jeff Bezos said in an interview with Geekwire, “I think health care is going to be one of those industries that is elevated and made better by machine learning and artificial intelligence.”
Problem: AI is overhyped and won’t live up to expectations.
While the survey results of 200 health executives by Intel and Convergys Analytics indicated that vast majority of them expected widespread AI adoption within the next five years, over half of them are skeptical about AI and think it will be implemented poorly or won’t work properly.
Solution: Have a clearly defined digital strategy.
We’ve all heard the saying, “failing to plan is planning to fail.” When it comes to successful AI adoption, it is crucial to collaborate across functions and verticals to create a robust vision upfront.
Make AI adoption everybody’s business by seeking the input of all departments across functions and verticals. Defining a digital strategy upfront ensures your AI transformation will help you rise above the competition.
Problem: What if I don’t see ROI on my AI transformation?
According to the PwC 2017 Global Digital IQ Survey, only 27% of health executives agreed that they effectively utilized all captured data to drive business value.
Solution: Lay the groundwork for data interoperability and quality while driving value by increasing revenue and reducing costs.
Artificial intelligence can deliver value by automating redundant human tasks, identifying trends in historical data, and improving decision making.
ROI metrics can be refined to include increasing revenue and reducing costs. However, before the AI can deliver meaningful solutions, the algorithm must learn from a vast pool of data that is standardized, labeled, and free of anomalies. Integrate AI insights back into your workflows by enabling the seamless exchange of data from one source to another. According to PwC research, 59% of health executives agree that big data will be improved with AI.
Filter the value of AI in healthcare innovation through the lens of increasing revenue and reducing costs. Ensure your insights are meaningful by training your AI with standardized, un-biased, data and enable the seamless exchange of those insights back into your workflows.
Conclusion:
The AI disruption of the healthcare industry is imminent, as executives predict its widespread adoption over the next five years. Successful integration means coordinating people, processes, and technology.
Let’s recap what we’ve learned:
Gain patients’ trust by ensuring transparency in all aspects of the technology. Be clear about the benefits and limitations to patients. Consider using AI to automate simple tasks before deploying it for more complex ones.
Foster a digital culture by being willing to take risks, experiment, and upskill talent not only to engage your staff but also ensure faster AI adoption. The most successful AI enterprises see AI as a way to augmenting your staff’s capabilities.
Define your AI-strategy upfront by involving all stakeholders across all functions and verticals in your organization. Have a clear vision of what success looks like and define value through metrics of increasing revenue and lowering costs.
Ensure the quality of your AI insights by making sure you’re feeding the algorithms with unbiased, standardized data. Enable the seamless exchange of data and flow of AI insights into your workflows by investing in interoperability.
The time is now for the AI transformation of healthcare. While fears of AI-innovation hold patients, staff, and even executives back, its adoption becomes increasingly widespread. Consider the steps outlined in this article to overcome the barriers to AI to let it set you apart from your competition.
