Portable MRI shows promise for detecting strokes in emergency settings, study finds

A portable, AI-powered MRI system designed to bring brain imaging directly to the bedside demonstrated strong performance in detecting strokes, including very small ischemic lesions, according to results from the largest data set yet evaluating the technology.

Hyperfine Inc. said a prospective, multi-center observational study published in the November issue of Stroke: Vascular and Interventional Neurology evaluated 95 patients and found that its next-generation Swoop portable MRI system significantly improved diagnostic accuracy and efficiency compared with earlier versions of the scanner. The data support the use of portable MRI for stroke detection in multiple clinical settings, including hospital emergency departments, where time is critical.

The study combined data from the ACTION PMR study at Massachusetts General Hospital and Buffalo General Medical Center with additional patients from Yale New Haven Hospital. Researchers assessed how well the Swoop system detected ischemic lesions using diffusion-weighted imaging, or DWI, a type of MRI sequence considered essential for identifying acute stroke. Performance of the original Swoop scanner was compared with a next-generation system using an advanced, multi-directional DWI sequence.

According to the findings, the next-generation Swoop system was able to identify lesions as small as 2.8 millimeters, or 0.15 milliliters in volume, allowing clinicians to detect very small strokes. For clinically relevant lesions larger than 1 milliliter, the system achieved 100% sensitivity and 100% specificity. Scan times were reduced by about 30%, and image quality across the brain improved, boosting diagnostic confidence.

“We previously showed that using DWI in combination with FLAIR on the portable MRI system can be used as a ‘tissue clock’ for stroke detection, similar to conventional MRI,” said Taylor Kimberly, MD, PhD, chief of the Neurocritical Care Division at Mass General Brigham. “With this study, we took the next step and evaluated the capability of ultra-low-field MRI with advanced, multi-directional DWI sequences to detect very small ischemic lesions. The results show that the next-generation portable MRI system with a multi-directional DWI sequence enables detection of very small strokes in a clinically feasible timeframe. The portable MRI system’s ability to detect clinically relevant strokes opens new possibilities for transforming stroke diagnosis and management—bringing timely evaluation to more patients and care settings than ever before.”

The Swoop system is an ultra-low-field, portable MRI scanner that can be wheeled to a patient’s bedside and plugged into a standard electrical outlet. Unlike conventional MRI machines, which are fixed installations requiring shielded rooms and patient transport, the Swoop system is designed for use in settings where a full diagnostic MRI exam may not be practical. Cleared by the U.S. Food and Drug Administration for brain imaging in patients of all ages, the system uses artificial intelligence to help reconstruct images of the brain that trained physicians can interpret to aid diagnosis.

“Stroke detection represents a critical driver of the Swoop system’s expansion into emergency departments,” said Maria Sainz, president and CEO of Hyperfine. “The results from our next-generation Swoop system, combined with our new, advanced multi-direction DWI sequence that was recently cleared by the FDA, are truly remarkable. This data gives us even greater confidence that the Swoop system can reliably detect clinically relevant strokes, streamline workflows, and further strengthen the value of integrating portable MRI into stroke diagnosis and care.”

Hyperfine said it provided portable MRI systems under sponsored research agreements but was not involved in the design or analysis of the investigator-initiated study or in the decision to publish the results.

Portable imaging and AI reshape stroke diagnosis

The findings come amid broader advances in stroke imaging and neurocritical care, where speed, access and precision increasingly determine patient outcomes. Stroke treatment is highly time dependent, with therapies such as thrombolysis and mechanical thrombectomy most effective when delivered within narrow windows. As a result, researchers and device makers have focused on technologies that can shorten the time from symptom onset to diagnosis.

One major trend is the integration of artificial intelligence into medical imaging. AI algorithms are now routinely used to accelerate image reconstruction, improve image quality from lower-field scanners, and automatically flag suspected abnormalities. In stroke care, AI tools have been deployed to detect large vessel occlusions on CT angiography, estimate infarct core size, and prioritize scans for rapid review by clinicians. Applying these techniques to ultra-low-field MRI has helped overcome historical limitations in image resolution and signal quality.

Another key development is the push to decentralize imaging. Traditional MRI scanners are expensive, immobile and often located far from emergency departments or intensive care units. Transporting critically ill or unstable patients to radiology suites can introduce delays and risks. Portable imaging devices, including mobile CT and MRI systems, aim to bring diagnostic capability directly to the patient, whether in the ED, ICU or even resource-limited settings.

Advances in MRI sequence design have also played a role. Improved diffusion-weighted imaging, faster acquisition techniques and better motion correction have made it more feasible to obtain diagnostically useful scans in shorter timeframes. For stroke evaluation, the ability to visualize small ischemic lesions and distinguish acute from older injury is particularly important for treatment decisions.

Together, these developments reflect a broader shift toward faster, more accessible neuroimaging that supports clinical decision-making at the point of care. As portable MRI systems continue to improve and accumulate clinical evidence, they may complement conventional imaging by expanding access to timely   stroke diagnosis in settings where it was previously difficult or impossible.

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New wristband offers real-time insights for diabetes and heart health

A flexible new wristband developed by engineers at the University of California San Diego could improve how people with diabetes manage their health by continuously monitoring glucose and key cardiovascular signals in real time. The technology, published in Nature Biomedical Engineering, combines painless microneedle sampling with ultrasonic and ECG sensors in a single wearable device.

The device samples interstitial fluid beneath the skin using a replaceable microneedle array, allowing for real-time monitoring of glucose, alcohol, and lactate. Simultaneously, it uses an ultrasonic sensor to measure blood pressure and arterial stiffness, while ECG sensors track heart rate. These metrics offer a more comprehensive view of health than traditional glucose monitors alone.

“Comprehensive and effective management of diabetes requires more than just a single glucose reading,” said An-Yi Chang, co-first author and postdoctoral researcher in the Aiiso Yufeng Li Family Department of Chemical and Nano Engineering at UC San Diego. “By tracking glucose, lactate, alcohol and cardiovascular signals in real time, this pain-free wristband can help people better understand their health and enable early action to reduce diabetes risk.”

The project was a collaboration between the labs of professors Joseph Wang and Sheng Xu. Wang’s team focuses on chemical biomarker detection, while Xu’s group specializes in wearable ultrasound devices. Their joint effort created a platform that integrates metabolic and cardiovascular monitoring for round-the-clock insights.

The wristband’s readings have shown strong alignment with standard commercial devices, including glucose meters, breathalyzers and lactate monitors. Researchers plan to expand its functionality and eventually power it through sweat or sunlight while integrating AI to analyze personal health trends.

A new era in diabetes wearables

The UC San Diego wristband reflects a growing trend in the health care sector: next-generation wearables that go far beyond step counts and heart rate. As chronic disease management increasingly moves outside clinical settings, researchers and companies alike are racing to develop smart, continuous monitoring tools tailored for real-world use.

One of the biggest recent advancements is the ability to measure multiple biomarkers simultaneously. Traditional continuous glucose monitors have already transformed diabetes care, but their scope is limited to blood sugar. Adding alcohol and lactate levels provides important context about behavior, diet, and exertion, while cardiovascular indicators like arterial stiffness and blood pressure reveal longer-term health risks often invisible to glucose data alone.

What sets newer devices apart is the integration of sensors once thought too bulky or complex for wearables—such as ultrasonic arrays and microneedles. Innovations in materials science and miniaturization are making it possible to incorporate hospital-grade diagnostics into discreet, user-friendly formats.

The use of artificial intelligence is also on the horizon. Future devices may analyze trends across thousands of data points collected every day, alerting users to early signs of heart disease, insulin resistance or poor recovery from exercise. Combined with telemedicine, these insights could help providers customize treatment or intervene before a crisis occurs.

As devices like UC San Diego’s wristband advance toward commercial viability, the healthcare sector is likely to see a surge in multi-sensor platforms that empower individuals to take more proactive roles in managing complex, chronic conditions like diabetes.

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Connecting a disconnected healthcare ecosystem with telehealth and technology

The latest international study on health systems from the Commonwealth Fund once again ranks the U.S. last among 11 high-income countries in providing equitably accessible, affordable, high-quality health care. In fact, since 2004, the U.S. has ranked last in every edition of the Commonwealth Fund report. It’s a paradox considering the U.S. spends more per capita than any other country. To provide historical context, health spending (inclusive of all health-related activities, including administration of insurance, health research, and public health) totaled $74.1 billion in 1970, $1.4 trillion in 2000, and $4.1 trillion in 2020. Between 2019 and 2020, the rate of increase was 9.7%, whereas the increase from 2018 to 2019 was half that at 4.3%.

Turns out that getting good, essential health care in the U.S. depends on income, more so than in any other wealthy country. The report revealed that 50% of lower-income U.S. adults stated costs as a barrier to needed health care compared to 27% of higher-income adults. In comparison, on average, only 12% of lower-income and 7% of higher-income adults in the other countries stated costs as a barrier. Here’s the irony, however: A person of high income in the U.S. was more likely to report financial barriers to care than a low-income person in almost every other country in the survey, which includes Australia, Canada, France, Germany, Netherlands, New Zealand, Norway, Sweden, Switzerland, and U.K.

To fix the increasingly disconnected healthcare ecosystem in the U.S., technology may bring it all together—for everyone, regardless of income. Consider telehealth for example, where a patient can avoid long wait times and network and geographical limitations. Regarding scheduling an appointment with a specialist, such as a dermatologist, a February 2022 report from the Vermont Agency of Human Services showed that patients in that state had a three-month or more wait time. Then there is the issue of in-network and out-of-network. Telehealth providers holding multiple licenses to practice in various states helps patients overcome these challenges. But that is just the beginning of the connectivity loop.

A first virtual visit with a telehealth provider can lead to a long relationship in managing and monitoring a specific condition, such as hypertension, at a distance. From embedded technology such as sensors in devices, to Bot sessions before the first visit—and after or in-between visits—technology is dramatically improving the ability of a doctor or health professional to monitor the patient condition, provide proactive solutions and help base decision-making over a personalized, wide and updated range of data about the patient. So actually, technology is reducing the barrier of access to the doctor while also boosting the medical team's attention to the individual's health, leveraging the ability to improve his condition dramatically (and reduce the related cost) for a fraction of the cost.


The number of smart, connected healthcare devices to aid in this regard is increasing rapidly. Take digital blood pressure monitors: Utilizing Bluetooth, some connect to an iOS or Android smartphone, tablet or smartwatch and send information to a specified provider. There are even smart pill bottles that are internet enabled, with embedded sensors that send data to the cloud (time cap is opened, closed and amount of medication dispensed), as well as send reminders via text message, email or automated phone calls. In addition to helping patients avoid mistakes in medication doses, a telehealth provider can review the information virtually, then analyze and make recommendations or changes. For patients with diabetes, a wide range of wearable glucose monitors check a patient’s levels with an easy scan rather than a finger prick, tracking patterns and enabling remote data sharing with a healthcare provider.

Mental health, in particular, is an area of healthcare that could benefit most from telehealth providers, many of whom treat depression, generalized anxiety disorder, panic disorder, social anxiety disorder, PTSD, OCD and insomnia disorder in adults using the Enhanced Evidence-based Care (EEBC) method. It combines Measurement-Based Care (MBC), Collaborative Care (CC), Algorithm-guided Treatment (AGT), and Integrated Care (IC) to optimize treatment delivery and outcomes. Clinical research has repeatedly found the EEBC method to be up to twice as effective as usual care. That’s good news for a country facing a mental health crisis. The key findings from a recent Mental Health America report are staggering:

• In 2019, before COVID-19, 19.86% (nearly 50 million) of adults experienced a mental illness.
• The national rate of suicidal ideation among adults has increased every year since 2011-2012.
• A growing percentage of youth are living with major depression, with 15.08% experiencing a major depressive episode in the past year.
• More than 2.5 million youth have severe depression, and multiracial youth are at the greatest risk.
• Over half of adults with a mental illness (more than 27 million) do not receive treatment.
• The percentage of adults with a mental illness who report an unmet need for treatment has increased every year since 2011.
• More than 60% of youth with major depression do not receive any mental health treatment.
• Nationally, fewer than 1 in 3 youth with severe depression receive consistent mental health care. Both adults and youth in the U.S. continue to lack adequate insurance coverage.

Especially for patients hesitant to get the mental health attention they need, telehealth may provide a safe place to begin a journey back to wellness. In fact, the most forward-thinking providers offer mental health as a key service, understanding the vital connection to overall physical wellbeing, especially for those who suffer from other chronic conditions.

For underinsured or uninsured Americans, telehealth providers that will offer affordable, high-quality care either through a subscription model or pay-by-visit can be the difference between life and death for patients that can access services on demand from anywhere, with little to no waiting time,. With advances in technology, there is every reason to believe the U.S. healthcare system can be salvaged, if not completely overhauled. In June 2022, a panel of health experts backed by the Commonwealth Fund recommended that a new national public health system be created to fix the disconnected healthcare model. This will require aligning policies, legislation and technology that prioritize affordable access to quality care for every patient. The good news is that connected products and services to enhance healthcare virtually the home are on the rise. Moving forward, there is no doubt that collaboration and commitment of all stakeholders, including the private, public and government sectors, will be necessary to create a new ecosystem in order to realize long-lasting success and the most remarkable patient outcomes imaginable.

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The evolution of symptom checkers

Patient symptom checking has been a common occurrence since the early days of the internet. Over time, this universal experience of “Googling” one’s symptoms has led to the development and improvement of clinically-validated symptom checking tools. Despite their broad acceptance and use by our patients, many of my physician colleagues remain hesitant toward their formal integration into the healthcare system.

Evolution of Symptom Checkers

To understand physicians’ perspectives on symptom checkers, we must first understand their evolution. The earliest automated symptom checkers used cumbersome and often inaccurate decision tree models that took excessive time asking irrelevant and redundant questions. Over time, as technology has advanced, so too have these symptom checking tools.

Over the last several years, the most competitive symptom checkers have begun to use some form of artificial intelligence to enhance their performance — i.e. make more accurate diagnoses and a more efficient user experience for patients and providers. As specific types of AI models like adversarial, convolutional, recurrent and transformer-based neural networks continue to progress, AI will increasingly play a role in enhancing the efficiency and accuracy of diagnostic technology.

Clinician Apprehension to Symptom Checkers

There are many reasons why clinicians are concerned about the use of symptom checkers. The first is that inadequate symptom checking tools may steer patients toward incorrect diagnoses, potentially over or underestimating the level of care they need. This can often result in friction and skepticism towards clinicians when their time comes to assess and diagnose.

In addition, some clinicians may take umbrage at the use of symptom checkers because they feel that they contribute to the lack of trust between clinicians and patients. For example, if a patient is not confident in their physician's diagnostic skills, they will use symptom checkers as a crutch to make up for this self-perceived deficiency. Worse yet, some clinicians fear that their widespread adoption is an attempt to use technology to replace them. Although some of these concerns are valid, no matter how advanced symptom checkers become, they will always lack the fundamentally human element required to deliver complex care.

Potential For Improving Healthcare

Despite skepticism, symptom checkers hold enormous value for various reasons. Symptom checkers can be an incredibly useful tool for physicians in assessing what patients are concerned about and what they are feeling. When physical evaluations are not immediately possible, symptom checkers provide an approximation of underlying conditions and deliver a high-quality estimate of acuity.

Additionally, as symptom checkers advance and become more interoperable with other aspects of the digital front door and greater healthcare IT ecosystem, they can play an important role in triaging and directing patients to the right care, faster, before their condition worsens. This limits the burden on primary care physicians and their staff, and improves the financial and operational efficiency of the entire healthcare system. For example, emergency rooms are often overburdened with patients experiencing less severe symptoms. With the use of symptom checkers, some of these patients can, instead, be guided to telemedicine or other less urgent care settings.

The Future of Symptom Checkers

Although AI-powered symptom checkers will never fully replace physicians, healthcare technologists will (and should) continue to integrate these tools into the patient journey. This, along with an increasing acceptance of digital healthcare, has resulted in a growing number of EHR, patient engagement, and data companies attempting to create comprehensive, accurate, and easy-to-use symptom checkers.

What separates “the best” symptom checkers from “the rest” are the AI being deployed, and the algorithms and training sets used to build their knowledge base. One of the most promising models within diagnostic AI is the use of Sophisticated Bayesian Networks, built to assess probabilities, and mimic physician thought processes in taking a patient’s medical history.

As medical school students often do when taking their first history on a real patient — using a great deal of time to ask every question they ever learned germane to the patient’s complaint — so does the Bayesian model. With more experience, or patient data in the case of symptom checkers, this process becomes increasingly streamlined. Over time, both learn what follow-up questions are the most valuable in winnowing down the list of potential and probable diagnoses. Like the clinicians themselves, the more clinical encounters these models have, the faster and more accurate they will be.

What this means for clinicians moving forward

Symptom checkers will never replace clinicians. The proper physician-patient relationship involves a level of empathy that symptom checkers cannot provide. The ideal role of symptom checkers will likely always be to provide support to physicians in streamlining information flow, minimizing errors, and maximizing efficiency. When done well, symptom checkers will seamlessly blend into the clinical workflow to make the point-of-care encounter as productive as possible, allowing the physician and patient to devote more time to the human experience so integral to healthcare.

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Three steps to fixing the data quality issue plaguing the U.S. health care system

Data quality is essential to improving the U.S. health care system. But what is data quality? Basically, data quality is about extracting value for patients, clinicians and payers. High-quality data is both usable and actionable. In contrast, low-quality data, such as duplicate records, missing patient names, or obsolete information, creates barriers to care delivery and billing/payment while costing money across the health care system through inefficiency.

Unfortunately, health care has lagged virtually every other industry in leveraging data for strategic and operational advantage. This is not for lack of data. More patient health data than ever is being captured today by medical equipment, digital devices and apps. Indeed, if data is gold, health care is sitting on top of a goldmine.

Yet the industry generally has done a poor job of mining and refining this gold ore of data to a point where it can be useful. That is a wasted opportunity because refined, quality data would provide value to all health care stakeholders—hospitals, payers, health information exchanges, labs, and patients.

Impact on research, patient outcomes

Poor data quality has negative ramifications throughout health care. The way we bring new medications to market is a great case in point. The first phase is identifying the medication, a process that comprises less than 20% of the cost of drug development. Next comes clinical trials, which account for the vast majority of drug development costs.

Clinical trials are long and arduous. Worse, from a data quality standpoint, data still are collected on paper and then transferred to computer, and typically not associated with other data sets for similar types of patients or even other patients in that clinical trial. If all this information were easily available and everything was automated, clinical trials would cost less than 10% of what they do now.

I just started participating in a clinical trial and it is amazing how much effort is required just to log in to an older system for a single trial. There may be six to 12 different logins and each requires special access permissions. It can take several days to log in to just one of these systems. All that information under these logins should be united to become quality, actionable information that's easily available.

Even in the physician's office there's a significant lack of aggregated data for patients, leaving clinicians knowing only part of a patient’s story. And when a doctor sees a patient at the hospital, that doctor needs the patient’s ambulatory outpatient records to make sound, evidence-based decisions about appropriate treatment. In far too many cases, however, the data that would inform clinicians at the point of care are trapped in siloes scattered across the health care landscape.


Healthcare has not availed itself of advanced digital technologies such as artificial intelligence (AI) and machine learning (ML), which are being used to transform many other industries. In large part this is because its either not accessible or the quality of health data is so poor that intelligent machines would struggle to process and analyze it for actionable insights into patient care.

In contrast, quality health care data creates scenarios in which AI/ML quickly can provide clinicians at the point of care with information that enables them to educate patients about their specific condition, offer referrals to appropriate specialists, suggest new medications, and improve outcomes. With the right data and insights, clinicians also might be able to match the patient with a clinical trial that could save the patient’s life.

Low-quality data is a problem for payers because they need data to make decisions, whatever it’s condition. It starts when the patient gets a diagnosis and the payer may not be aware of it for weeks. So a patient diagnosed with cancer might suddenly have to find a specialist and determine whether the practice takes their insurance.

But if a payer finds out right away that this patient may have cancer, the payer can help guide patients to the right provider, removing a heavy burden of financial concerns and medical management.

Three steps to quality data

There are three main steps to achieving high data quality. The first is ensuring access to the data the clinician needs. There are still many legacy systems in health care where the vast majority of medical records are housed within an organization’s servers and not on a cloud, where they could be more easily accessed and aggregated by authorized users. While health care interoperability and data sharing have improved in recent years, we’re still building integrations one at a time. Bringing data into a central database where it can be turned into gold remains a challenge.

The next step involves identity management. Much of the value in health data comes from its ability to document longitudinal change in individual patients. Clinicians may, for example, want to see how medications or disease processes affect lab results for a particular patient. Even if they could connect disparate data sources and have the data go into a single database, they can't associate the data with a specific patient. With effective identity management, though, it is possible to create a good longitudinal patient record that can bring huge clinical and efficiency benefits.

The third phase of data quality will finally make the data usable or actionable. Once the data is in the right patient's chart and created a longitudinal record, it must be organized and easy for clinicians to locate and read. This requires a process called data normalization. Health data can come from multiple sources (EHRs, labs, pharmacy systems, etc.), all of which may use different coding for a medical procedure, different terms for a certain test, or even use different language to categorize genders. Data normalization creates a common terminology that enables the semantic interoperability necessary to make data actionable.

There’s no magic bullet for improving health care data quality. It will require a joint effort and the innovation inherent in the free market. There will be companies that will help us collect and connect to data and there will be companies and entities that will help us connect to data. There will be technologies that will identify the data and others that will normalize the data so it looks similar—and becomes actionable—no matter where it originated. And there will be companies that provide quality databases that health care stakeholders can use to perform advanced analytics. The end result will be a health care system that provides better care at a lower cost.

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