Helping At Home Healthcare Patients with Artificial Intelligence


Until very recently, a caregiving parent possessed few at home health tools beyond a simple thermometer. Then, as the internet developed, so too did online healthcare sites such as WebMD, offering another very powerful tool — information. At home health tools continue to rapidly undergo massive changes, and now it’s AI leading the way. Today a parent can look inside their child’s ear and receive help treating an ear infection, or an elderly person can conduct their own hearing test without ever leaving the house, and often, with intelligent machines operating behind the scenes. Increasingly smart at home health devices are evolving through the rapid proliferation of AI and the increased embrace of digital medicine. These new tools include devices like smart stethoscopes that automatically detect heartbeat abnormalities or AI powered otoscopes that can look in a person’s ear and detect an infection.

Imagine a world where at home AI healthcare tools get smarter and more able to heal you every day. These tools are incredibly data driven — where they are continuously collecting data off your body, about your environment, your nutrition and activity — and then these algorithms are continuously learning from this data

not just from you, but from millions of other patients and doctors who know how to make sense of this information.

These AI tools will then deliver personalized healthcare tips and remediation throughout your whole life. Perhaps one day without you having to set foot in a brick and mortar hospital.

AI can help wherever the care provider is identifying patterns, for example whenever a physician identifies the acoustic pattern of a heart murmur, the visual pattern of an ear infection image, or the contours and shapes of a carcinogenic skin lesion.

What if AI could help you or a doctor predict a deteriorating heart condition? “If you can go to the hospital and say, ‘I’m about to have a heart attack,’ and you have proof from an FDA-approved product, it is less costly to treat you,” said author and ABI Principal Analyst Pierce Owen (1). Other at home healthcare tools are becoming smarter everyday with tools such as EEG Headbands that can monitor your workout and vitals, Smart Beds and devices such as EarlySense that detect movement in your sleep and give you detailed data driven reports on a variety of vitals and how much sleep-and deep sleep-you are actually getting or smart baby monitors that allow parents to monitor newborn vitals. (2)(3)(4)

One significant way AI at-home healthcare is taking off is by helping parents with young children.

Parents can never get answers quickly enough when something is wrong with their child. So what if they never even had to drive to the doctors office?

According to the National Institute of Deafness and Other Communication Disorders (NIDCD), 5 out of 6 children experience ear infections by the time they are 3 years old. That’s nearly 30 million trips to the doctor’s office a year just for ear infections in the U.S. alone. Additionally, ear infections cost the US Health System 3 billion per year.


This is where companies like Cellscope step in. A pioneer in the otoscope industry, Cellscope has had success launching it’s otoscope, Oto Home. Oto Home is a small smartphone peripheral device that slides onto the users iPhone accompanied by an app. Once inside the child’s or patient’s ear the app's software recognition feature called the Eardrum Finder begins to direct the user to move and tilt the scope to capture the visuals a physician will need to attempt a diagnosis. After the session, the user enters some basic information about the patient and both the recording and the information is sent to a remote physician who reviews the data and if necessary can prescribe medication.(5) This same image used by the remote physician, can, be used by an artificial intelligence system to assist the physician with a diagnosis. The use of the AI system can decrease the costs of more expensive tests, in addition to identifying more refined possible diagnoses.

AI in healthcare can now also detect heartbeat abnormalities that the human ear cannot always initially detect. Steth IO captures exactly the premise of what the company’s goal is: “see what you cannot hear”. One study found that doctor’s across three countries could only detect abnormal heart sounds about 20% of the time.(6)

By using thousands of various heartbeat sounds, our Xyonix data scientists trained the Steth IO AI tool to “learn” how to tell which sounds are out of the norm. After the system takes in the encrypted and anonymized heartbeat recordings, it sends back a classification like “normal” or “murmur” to help assist the physician in their diagnosis.

Since patients can see and hear their heart and lung sounds, patient engagement is also a bonus for physicians. Steth IO also differentiates itself from other emerging AI healthcare tools by integrating the bell of the stethoscope directly into the iPhone so there is no need for Bluetooth or pairing and it displays all results in real time (8).

While this is currently only operated by physicians, as the at home healthcare space rapidly grows, we expect to see similar heartbeat abnormality detection abilities tailored for at home use so that you can check the health of you and your loved ones.


Virtual AI driven health care systems are also quickly making their way into people’s homes. Take for example HealthTap, which brings quality medical service to people around the world who lack the ability to pay. How it works: patients receive a free consultation via video, voice, or text. Then,

“Dr. A.I.”, their new artificial intelligence powered “physician”, converses with the patient to identify key issues and worries the patients is having. Dr. A.I then uses general information about the patient and applies deep learning algorithms to assess their symptoms and apply clinical expertise

that attempts to direct the user to an appropriate type and scale of care. (9)

Dr. AI isn't the only new AI that can give you healthcare advice from the comfort of your home. CareAngel launched its AI virtual nurse assistant, Angel. Their goal is to reduce hospital readmissions by continuously giving medical advice and reminders between discharges and doctors visit. Healthcare providers can also use angel to check in on patients, support medication adherence and check their patient’s vitals. (10) Ultimately this AI technology strives to significantly reduce the administrative and operational costs of nurse and call center outreach.

In a world where healthcare is meeting resistance from rising costs, we can see that the emergence of innovations in AI and digital health is expected to redefine how people seek care and how physicians operate. The goals and visions of most emerging health companies currently are simple:

allow new suppliers and providers into the healthcare ecosystem, empower the patient and provider using real-time data and connection and take on lowering general and long-term healthcare costs.

While healthcare has always been patient centered, AI is taking patients from a world with episodic in clinic interactions to more regular, on demand and in home care provider / patient interaction.

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Transforming Radiology and Diagnostic Imaging with AI

AI is transforming medical applications in radiology and diagnostic imaging by, in essence, harnessing the power of millions of second opinions.

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By training a new generation of machine learning models using the expertise of millions of highly trained and experienced physicians, AI models are increasingly outperforming any one doctor at many medical imaging tasks.

Without knowing a lot about what’s going on in medicine, some might think that new AI tools “just help” a doctor to look at an image, or listen to a breathing pattern, in that diagnostic moment. What clinicians understand, though, is that real clinical work exists in the context of “layers” - signals over periods of time, scans that slice the onion of the human head from top to bottom in thin segments, and other sophisticated types of new radiology and clinical testing that actually deliver quite a lot of “big data.”

Physicians aren’t just looking at “a picture” - more likely, a team is racing to sort through the layers of an MRI, or studying real-time heart rate and blood pressure data to try to work backward through a patient’s medical history.

In these types of situations, when the chips are down and the patient is on the table, ER teams, surgeons and other skilled medical professionals know that quick diagnosis and action is often the difference between life and death - or between a full recovery and paralysis or other deficits.

A detailed piece in the New Yorker last year shows how much of this technology is sought after and driven by a desire to save patients, as in the case of Sebastian Thrun, known for his tenure at Udacity and his work on driverless cars, who also went to work on medical AI to try to promote early diagnosis of early-stage cancers.

Reading articles like these that show clinicians at work, we understand that machine learning and artificial intelligence are building new models for interventionary care that will change how we see medicine in the near future. AI will never “replace” the radiologist - instead, it will inform doctors, speed up their work, and enhance what they do for patients.

What is Diagnostic Imaging?

The category of services known as ‘diagnostic imaging’ encompasses many different methods, tools and uses.

Diagnostic imaging includes x-rays, magnetic resonance imaging, ultrasound, positron emission tomography or PET, and computed tomography or CT scan, along with more specialized or smaller fields of diagnostic imaging, such as nuclear medicine imaging and endoscopy-related imaging procedures.

What all of these types of procedures have in common is that they look inside the body and provide a great opportunity for gaining insights from pattern recognition.

Radiology and diagnostic imaging enables us to peer inside layers of bone and tissue to spot conditions or changes happening nearly anywhere in the body.

Scientists of past centuries would have marveled at the many ways we effectively explore the inside of the human body to gain deep insights on treatment options.

Many Scenarios, Many Diseases

There’s a reason that every emergency department in the U.S. has some type of radiology testing installed on premises - it’s because the applications are diverse and radiology enables so many different kinds of interventionary work.

Doctors use different types of diagnostic imaging to look at bone breaks and figure out how to fix fractures or other tricky problems related to muscles, ligaments and tendons. They use diagnostic imaging in oncology to identify or track tumors. In traumatic injury situations, doctors can quickly scan parts of the body to learn more about the extent of the injury itself, and likely outcomes. And they use diagnostic imaging in evaluating patients in all stages of life, from the developing fetus to the geriatric patient.

The Life Cycle

Just as diagnostic imaging is used for many types of diseases and conditions that exist all over the body, it also gets used throughout a complex “life cycle” of evaluation.


This begins when a patient is initially seen by a healthcare provider, and the doctor orders a first diagnostic test. Medical experts will either find actionable results or not. If they do, the life cycle of tracking a particular condition begins, whether it's a growth (benign, malignant or unknown), a fracture, or some other kind of condition, and observations of its development help in forming a positive or a negative prognosis.

Throughout the diagnostic related care cycle, physicians are observing and understanding patterns. Pattern recognition is the key task for understanding the results of clinical scans.

For example, in assessing bone structures, the radiologist is looking carefully for not only evidence of a break or fracture, but evidence of a specific kind of break, and accurate locational details. Radiologists look to identify a complete, transverse, oblique, or spiral fracture, along with other kinds of complex breaks like a comminuted break or greenstick fracture. They also try to locate the affected area of the bone, assessing things like diaphysis, metaphysis and epiphysis for damage.

All of this requires detailed visual assessment of a complex pattern to map and structure what the radiologist sees.

Likewise, the radiologist in oncology will be looking at tissues in the body at a very detailed level, to spot the delineations of a biomass and try to predict its type (benign, malignant, etc.) and look at adjacent types of tissue that may be affected.

So how does AI and machine learning apply?

Supervised machine learning models work by learning a highly complex mathematical mapping through a large number of training examples

(for example lots of pictures of something like a growth, and a classification for each like whether it is benign or not). In this way, a machine learning model learns to “interpret” visual data accurately.

A helpful paper on “The Building Blocks of Interpretability” provides a rich explanation of feature visualization (what the models are using to form this mapping), dimensionality reduction (how to prevent confusion from too many variables) and other techniques that ML uses to fascilitate interpretation - but unlike many other sources, this paper visually shows how layers of mathematical neurons interpret many iterations of a picture, to come up with detailed labeling that supports pattern recognition. Like the radiologist’s observation, the machine learning interpretation process is intricate, sophisticated, a gentle dance of identification and meaning.

AI Helping in Multiple Practice Areas


In oncology, AI tools are helping to improve the diagnosis and treatment of many types of cancers – computer scientists are increasingly using neural networks to stage cancers, and to understand applications related to gene expression and other treatment options.

They’re also using artificial intelligence for tumor segmentation – in tumor segmentation, doctors seek to specifically delineate the different types of matter in the tissue, including solid or active tumor and necrotic tissue. They also identify any normal or healthy tissue and any other substances such as blood or cerebrospinal fluid that may be adjacent.

Neural networks show strong promise in predicting cancer and segmenting specific tumors for breast cancers, rectal cancers, and other categories of cancer that affect many Americans each year.

[Neural Network Techniques for Cancer Prediction, Procedia Computer Science]

Again, this is essentially a pattern recognition process. Tumor segmentation requires detailed manual segmentation requiring significant labor-intensive work looking. Many experts need to use a tool like Vannot to identify the precise contours of individual tumors and their cancer state. These annotations then enable a deep learning network to be trained so that it can act like the experts, and also outline tumors and determine whether they are benign or cancerous. It's something AI excels at and automates to a high degree and ultimately gives doctors powerful new tools to assist in diagnosis.

Doctors can also use artificial intelligence tools to detect pneumonia as in this Cornell University library paper where a technology called CheXNet outperformed a team of radiologists in some aspects of pneumonia diagnosis. The paper shows how visual patterns in various lobes of the lung area are indicative of pneumonia.

Machine learning technologies can also assess the actual physical brain to predict risk for neurological conditions, as in this 2015 paper by Adrien Payan and Giovanni Montana that explores using neuroimaging data for Alzheimer’s diagnosis.

The Eye as the Window to the Body

AI can also help with “derived diagnosis” – where data from one part of the body tells us about an entirely different part of the body. Consider, for example, this report on Google AI. Google’s new software can look at eye imaging, and spot signs of heart disease. This type of “cross-platform” analysis adds critical tools to the clinician’s arsenal.


Also, heart disease is not the only health problem that eye scans can predict. Doctors are using scans like retinal scans, iris scans and something new called Optical Coherence Tomography or OCT to review patients for all sorts of reasons, such as diagnosing glaucoma or retinal issues, or secondary conditions like diabetes that can trigger changes in the eye.

Some other uses of optical scans are meant to assess patients for mental health issues. Schizophrenia is one such malady that scientists are suggesting can be partially diagnosed, predicted or indicated through eye movement. The emergence of “eye tools” to capture eye movement or other optical data and feed it into ML/AI platforms constitutes one of the best examples of this technology at work.

All Medicine is Data-Driven

Even in some of the radiology applications that you wouldn't necessarily think of as pattern-driven, artificial intelligence can play a big role.

One of the most common examples of this is an ultrasound for OB/GYN doctors. In the course of a pregnancy, doctors typically have a number of scans that show fetal development – and you think of the results as something that’s neat to show the family, or something that’s used as a general assurance that the fetus is developing properly. But to doctors, this isn't just a binary evaluation. They're not just looking for whether the fetus is okay or isn't okay – they're looking at very detailed things, like the amount of amniotic fluid in the scan, and the exact positioning of the fetus as well as its constituent parts.

With all of this in mind, artificial intelligence enhances what clinicians can do and enables new processes and methods that can save lives.

Human and Machines in Collaboration

With these technologies, and with that very important human oversight, we are increasingly leveraging the enormous power of human and machine collaboration.  There’s a wealth of potential when humans and machines work together efficiently -- you're putting the brains of smart doctors together with the knowledge base and cognitive ability of smart technologies: what comes out is the sum total of human and machine effort.

We humans have adapted symbiotically in the past. Consider the human driver seated on a plow, reins in hands, managing and leveraging the manual power of horses to till.

This can be a very instructive metaphor when it comes to the collaboration AI technologies provide. Developing this synergy requires the creation of tight feedback loops, where expert clinicians' natural activities provide the data and instruction to machines, who in turn, tirelessly, and rapidly, reduce the burden of repetition and open the doors to higher efficiency and efficacy.

It’s essential to get all of the right data in play. Companies like Xyonix working on the cutting edge of medical AI, tap into the data sources - for instance, from medical sensors like a digital otoscope, or clinical IT systems like EHR/EMR vendor systems. When all of this comes together seamlessly, it opens the doors to a whole host of powerful innovations. That’s something exciting, and at Xyonix, it’s something we are proud to be a part of. AI is re-inventing radiology in terms of the quality and speed of diagnosis and the quality and speed of care. These are potentially life-saving and life-enhancing technologies. The goals of any health system is to improve outcomes, and with the addition of new tools and resources, the medical world is taking great strides in the business of healing.

Need Help with Your AI Needs? CONTACT us -- we might be able to help.


Helping Physicians with AI, The Data Science of Health

Promising AI powered physician assistance tools are exciting because they change work models clinicians use to treat patients, improve medical outcomes, and save lives.


New medical technologies can seem like science fiction. For instance, if you've ever watched Star Trek, you likely saw characters use a “tricorder,” a device that can ‘scan’ individuals for signs of disease or conditions, interpret diagnostic information, and sometimes take corrective action -- all in more or less in real time.

The average Star Trek fan might not realize that many capabilities of the tricorder actually exist now.

New physician assistance tools use a similar model, based on the ability to combine various functions to streamline or automate medical work.

One way to think of this is in terms of three broad functions – the first one is the collection and aggregation of information, often through sensors. Sensor-based technologies have been around for a while, but they're quickly taking off in healthcare while being paired with other tools. They’re also evolving in how they collect health data. One example is the abundance of current tools that record physiological functions like heart rate in real time. Just a few years ago, these technologies were not widely available. Their recent emergence has brought vast change to healthcare, in the treatment and diagnosis of conditions like atrial fibrillation, and in general efforts to figure out whether a patient is experiencing either tachycardia or bradycardia, whether immediate or chronic.

The second type of broad function takes this data and transforms it into insights. In the medical world, this is often focused on diagnosis. Data by itself is not inherently meaningful, unless it assists a pattern of comprehension. Artificial Intelligence models excel at pattern recognition and are built to understand, and often to present, patterns for easier recognition to humans.

The third function transforms these insights into actions – by orienting or focusing clinical decisions and clinical work. This can mean training machines to provide relevant results – and training doctors to harness these results.

When both humans and machines are trained effectively, the collaborative results can be impressive.

Today's technologies don't look just like what's on Star Trek, but they have some of the functionality built in, and there's always the potential to enhance and improve on these capabilities over time.

The advent of machine learning and artificial intelligence, and the progress made over the last few years, has the potential to contribute to better medical outcomes for millions of patients. These new tools are based on a very different fundamental philosophy of care -- the idea that capable decision support tools can help doctors to improve their accuracy, and enhance what they can do in the exam room and in the operating room.

One of the best ways to judge how important new AI-driven medical systems are is to look at the numbers in terms of dollars spent. A study from Accenture shows current spending estimates of $40 billion for robot-assisted surgery, $20 billion for “virtual nursing assistants,” and $18 billion for “administrative workflow assistance.”

As the presentation of the study points out, these segments are generating this kind of capital for a reason; they’re bringing in revenue for adopters. That’s because they’re driving superior outcomes, advancing what clinicians are able to do in their fields.

Teamwork in Medicine

In some ways, the new use of machine learning in physician assistance programs echoes other types of progress that practice administrators are making in the medical world.

Today, when you visit a specialist, you're more likely to meet with a physician assistant than you would have been ten or twenty years ago (as seen in this resource from Barton Associates). These PAs are credentialed and qualified for specific kinds of clinical work, to assist the primary medical doctor.

Using this care model frees up valuable resources -- it enables the specialist office to see more patients, and to treat and consult with patients in more specific ways. For example, a skilled surgeon may spend more time in surgery. Meanwhile, the practice is typically able to provide a comparable level of care to patients – or in many cases, an elevated standard of care. Machine learning tools like those offered by Xyonix further enhance this process.

AI systems often serve as additional ‘team members’ of the practice structure -- this team member just happens to be extremely good at assimilating vast stores of knowledge and delivering insights extremely fast without ever tiring.

If human PAs are part of the doctor's team, so are the physician assistance software AI models that are doing more in the clinical world. The AI systems may be checking x-rays or scans to look for key indicators of a particular diagnosis. They may inspect skin for signs of cancer. They may listen for symptoms and signs of disease in audio streams. Whatever the AI systems are doing, they are contributing to the specific way a practice has set up its services to triage patient care -- to make sure that each particular patient gets exactly what he or she needs at a particular moment.

In addition, ML/AI tools like those we make at Xyonix are made to enhance human teamwork processes as well. Think of a surgeon who can get reviews from experts and others beyond the hospital walls, or a specialist who can converse with other specialists to figure out a tough diagnosis.

If you watch a doctor making the rounds and observe their interactions with patients and the patient's extended care group, you see that a physician operates in a team. Physicians consult one another and other care staff in myriad ways -- these team interactions contribute heavily to a physician's clinical decisions. Typically, however, a doctor is limited to the team that's physically in the hospital at the time. Physicians can refer to notes from other teams of doctors, but they often can't conveniently converse or discuss things with absent doctors. In traditional medicine, communications have been delayed for the purposes of consulting teams – and the speed of clinical decision can move quite slowly.

Many physician assistant tools help crowdsource input in sophisticated ways – they not only provide broad medical opinions based on large data analysis and statistics, but they often incorporate feedback a doctor gets while examining a patient, medical record or during the course of a clinical interaction. This crowdsourcing increasingly provides instructional training examples that power AI systems.

EMRs/EHRs and Beyond – Not Just a Template

Not too many years ago, the healthcare world was abuzz over electronic health record and electronic medical record technologies. There was a lot of excitement about how these digital platforms could help improve clinical care and treatment.

These technologies essentially provide digital interfaces for documenting patient information. In some small ways, they started to assist doctors, but often not on an insight-driven basis. Some of the smartest features of electronic health records were templates that would help doctors to input a common diagnosis -- or auto chart fillers that could help doctors choose the language and dictation content that they needed to fill out a patient chart.

The key is that none of this was driven by anything particularly intelligent. The templates and automation tools were all geared toward rote data entry. They did help doctors to streamline care documentation -- but that's mostly where their utility ended.

Nonetheless, through the HITECH act and related initiatives, the government promoted the use of these new digital tools as one of the first steps toward fully modern and futuristic medicine.

AI powered physician assistance software is transcending EHR tools -- artificial intelligence increasingly helps doctors better understand an individual patient's condition and treatment options.

In addition to improving individual patient care, machines also help physicians effectively treat a broader community of patients. There are different ways to affect AI driven progress, and some rest on particular approaches that match a given task. One common approach involves the technology of natural language processing.

You might call this the “physician talk” model – but it applies to both voice and data, although mining natural language for information can be easier with text than it is with voice. In voice, there’s the extra step of transcribing the audio to determine meaning and intent -- recent deep learning models trained on increasingly large volumes of data have made remarkable progress in accuracy.

NLP models, or parsers, can learn to understand what physicians are saying in dictation, as well as when they are writing into a chart. Machines are increasingly able to listen – and make use of what they hear. This passive data aggregation is a very important part of what’s behind some of these technologies – for instance, physician assistance tools can report insights to doctors, based on what they've said in the past. That might sound like a simple task, but it’s actually a powerful way to authenticate clinical work. Doctors are only human, and work according to their perceptions in linear time, typically seeing many patients in a given day. These technologies, on the other hand, can present wide aggregated data that condense fields of study into a unified perceptive model. For example, physicians might state whether a patient is exhibiting particular symptoms many different ways. Normalizing these permutations into a single representation enables a higher level aggregation fundamental to gaining a deeper understanding of symptom rates across a wide population of patients and physicians. 

Another model could be described as a "records-based” model – think about electronic medical records and the types of information they contain. How do you mine that information effectively?

Machine learning programs can tag bits of natural language for classification – by building highly complex classifications, they can see, for example how prescription drugs are prescribed to patients, what doctors find in examinations and consultations, and other key bits of information that can be replicated across an enormous number of charts.

Any discussion of physician-assisted tools wouldn’t be complete without image analysis and computer vision – diagnostic radiology is  an enormous and growing field within the medical industry. Doctors are relying on different types of images and scans for all sorts of clinical work, and AI methodologies that can help are going to be vitally important to new healthcare workflow models.


When machines are applied to the scans and images, new technologies can be immensely effective in reading them in detail. A convolutional neural network, or CNN, can often provide excellent results that can, again, be extended across an infinite numbers of cases – this is the type of technology that's often in use when assisting physicians in assessing some visual items found in the scan – a cancerous lesion, or an outcome from invasive surgery, or anything else that can show up in CT scans, MRIs, x-rays or other types of diagnostic imagery.

Yet another model is a memory model that can be used to track clinical care. When nurses perform important interventions on patients, from tests to IVs and central lines, these actions could be recorded accurately in a comprehensive care narrative. Machine learning systems with memory, such as an LSTM setups of a recurrent neural network can be taught to “know” what has happened in a patient’s room, and deliver that on a timeline to clinicians and other stakeholders.

The Bedside Manner

Used correctly, new machine learning healthcare tools can provide a source of assurance for patients.

Patients need to trust their physicians, and many have an emotional connection with their doctor. Patient's also trust their doctors to harness modern technologies and evidenced driven care practices. People don't want to hear a diagnosis from a machine, but many of them might like to know their doctor has consulted an AI model trained by millions of top rate physicians proven to markedly outperform the average physician.

So when the medical provider can show that they have this kind of resource, it gives the patients and their families more peace of mind. It impresses on them that the medical business has the skill and ability to help their loved one through whatever condition he or she is facing.

Effective Use Cases

One important component of creating the best artificial intelligence PA applications is understanding when these tools can be the most helpful.

Think of it this way: when are doctors most like machines? When are physicians engaged in machine-like activities or behaviors? These are areas ripe for AI health innovation.

Clinical work is highly variable. Think of all of what a doctor might do in an average patient visit. Some of the core work is inherently “social” – doctors are explaining complex medical information to patients. That’s really not something that AI technologies should dominate -- at least, not yet, and perhaps, never.

On the other hand, when a doctor makes her way to the patient’s side and takes out a stethoscope – she gets quiet – and listens. At that particular moment, the work model switches from social to analytical. The doctor is then acting in a way that is “machine-like” – quantifying noise and substance in a signal pattern.

Those are the kinds of tasks to which artificial intelligence medical tools are well suited to assist. That’s a big part of what Xyonix is doing in the medical space – looking at these tasks, and automating them with a knowledge base and increasingly evolved AI.

Into the Future

Our new machine learning technologies are, by today's standards, pretty amazing -- but in many ways, they're really just the start.

There are all sorts of additional ways we can build on these ideas to give doctors new valuable insights -- we just haven't built them yet. We at Xyonix are contributing daily to this rapid progress -- this large leap into the future that will cause us to look back on the care of prior decades and marvel at what we've achieved and how far we’ve come.

These new care models will improve quality of life – they’ll increase longevity. They'll bring loved ones back to their families. They’ll do this, in general, by leveraging the power of distributed networks, the power of the medical community in general, and the resources that exist to fight disease, and bring them all of the way down to the individual point of care, the “front lines” where medical outcomes are created.

They’ll also help doctors to do more in a shorter period of time, which will help with the pressures and burdens put on top clinicians in the medical community. It's a win-win for the world, and we feel good about the work we’re doing to carry medicine into the future.

Need Help with Your AI Needs? CONTACT us -- we might be able to help.