SMART launches new Wearable Imaging for Transforming Elderly Care research group

What if ultrasound imaging is no longer confined to hospitals? Patients with chronic conditions, such as hypertension and heart failure, could be monitored continuously in real-time at home or on the move, giving health care practitioners ongoing clinical insights instead of the occasional snapshots — a scan here and a check-up there. This shift from reactive, hospital-based care to preventative, community and home-based care could enable earlier detection and timely intervention, and truly personalized care.

Bringing this vision to reality, the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, has launched a new collaborative research project: Wearable Imaging for Transforming Elderly Care (WITEC). 

WITEC marks a pioneering effort in wearable technology, medical imaging, research, and materials science. It will be dedicated to foundational research and development of the world’s first wearable ultrasound imaging system capable of 48-hour intermittent cardiovascular imaging for continuous and real-time monitoring and diagnosis of chronic conditions such as hypertension and heart failure. 

This multi-million dollar, multi-year research program, supported by the National Research Foundation (NRF) Singapore under its Campus for Research Excellence and Technological Enterprise program, brings together top researchers and expertise from MIT, Nanyang Technological University (NTU Singapore), and the National University of Singapore (NUS). Tan Tock Seng Hospital (TTSH) is WITEC’s clinical collaborator and will conduct patient trials to validate long-term heart imaging for chronic cardiovascular disease management.

“Addressing society’s most pressing challenges requires innovative, interdisciplinary thinking. Building on SMART’s long legacy in Singapore as a hub for research and innovation, WITEC will harness interdisciplinary expertise — from MIT and leading institutions in Singapore — to advance transformative research that creates real-world impact and benefits Singapore, the U.S., and societies all over. This is the kind of collaborative research that not only pushes the boundaries of knowledge, but also redefines what is possible for the future of health care,” says Bruce Tidor, chief executive officer and interim director of SMART, who is also an MIT professor of biological engineering and electrical engineering and computer science.

Industry-leading precision equipment and capabilities

To support this work, WITEC’s laboratory is equipped with advanced tools, including Southeast Asia’s first sub-micrometer 3D printer and the latest Verasonics Vantage NXT 256 ultrasonic imaging system, which is the first unit of its kind in Singapore.

Unlike conventional 3D printers that operate at millimeter or micrometer scales, WITEC’s 3D printer can achieve sub‑micrometer resolution, allowing components to be fabricated at the level of single cells or tissue structures. With this capability, WITEC researchers can prototype bioadhesive materials and device interfaces with unprecedented accuracy — essential to ensuring skin‑safe adhesion and stable, long‑term imaging quality.

Complementing this is the latest Verasonics ultrasonic imaging system. Equipped with a new transducer adapter and supporting a significantly larger number of probe control channels than existing systems, it gives researchers the freedom to test highly customized imaging methods. This allows more complex beamforming, higher‑resolution image capture, and integration with AI‑based diagnostic models — opening the door to long‑duration, real‑time cardiovascular imaging not possible with standard hospital equipment.

Together, these technologies allow WITEC to accelerate the design, prototyping, and testing of its wearable ultrasound imaging system, and to demonstrate imaging quality on phantoms and healthy subjects.

Transforming chronic disease care through wearable innovation 

Chronic diseases are rising rapidly in Singapore and globally, especially among the aging population and individuals with multiple long-term conditions. This trend highlights the urgent need for effective home-based care and easy-to-use monitoring tools that go beyond basic wellness tracking.

Current consumer wearables, such as smartwatches and fitness bands, offer limited physiological data like heart rate or step count. While useful for general health, they lack the depth needed to support chronic disease management. Traditional ultrasound systems, although clinically powerful, are bulky, operator-dependent, can only be deployed episodically within the hospitals, and are limited to snapshots in time, making them unsuitable for long-term, everyday use.

WITEC aims to bridge this gap with its wearable ultrasound imaging system that uses bioadhesive technology to enable up to 48 hours of uninterrupted imaging. Combined with AI-enhanced diagnostics, the innovation is aimed at supporting early detection, home-based pre-diagnosis, and continuous monitoring of chronic diseases.

Beyond improving patient outcomes, this innovation could help ease labor shortages by freeing up ultrasound operators, nurses, and doctors to focus on more complex care, while reducing demand for hospital beds and resources. By shifting monitoring to homes and communities, WITEC’s technology will enable patient self-management and timely intervention, potentially lowering health-care costs and alleviating the increasing financial and manpower pressures of an aging population.

Driving innovation through interdisciplinary collaboration

WITEC is led by the following co-lead principal investigators: Xuanhe Zhao, professor of mechanical engineering and professor of civil and environmental engineering at MIT; Joseph Sung, senior vice president of health and life sciences at NTU Singapore and dean of the Lee Kong Chian School of Medicine (LKCMedicine); Cher Heng Tan, assistant dean of clinical research at LKCMedicine; Chwee Teck Lim, NUS Society Professor of Biomedical Engineering at NUS and director of the Institute for Health Innovation and Technology at NUS; and Xiaodong Chen, distinguished university professor at the School of Materials Science and Engineering within NTU. 

“We’re extremely proud to bring together an exceptional team of researchers from Singapore and the U.S. to pioneer core technologies that will make wearable ultrasound imaging a reality. This endeavor combines deep expertise in materials science, data science, AI diagnostics, biomedical engineering, and clinical medicine. Our phased approach will accelerate translation into a fully wearable platform that reshapes how chronic diseases are monitored, diagnosed and managed,” says Zhao, who serves as a co-lead PI of WITEC.

Research roadmap with broad impact across health care, science, industry, and economy

Bringing together leading experts across interdisciplinary fields, WITEC will advance foundational work in soft materials, transducers, microelectronics, data science and AI diagnostics, clinical medicine, and biomedical engineering. As a deep-tech R&D group, its breakthroughs will have the potential to drive innovation in health-care technology and manufacturing, diagnostics, wearable ultrasonic imaging, metamaterials, diagnostics, and AI-powered health analytics. WITEC’s work is also expected to accelerate growth in high-value jobs across research, engineering, clinical validation, and health-care services, and attract strategic investments that foster biomedical innovation and industry partnerships in Singapore, the United States, and beyond.

“Chronic diseases present significant challenges for patients, families, and health-care systems, and with aging populations such as Singapore, those challenges will only grow without new solutions. Our research into a wearable ultrasound imaging system aims to transform daily care for those living with cardiovascular and other chronic conditions — providing clinicians with richer, continuous insights to guide treatment, while giving patients greater confidence and control over their own health. WITEC’s pioneering work marks an important step toward shifting care from episodic, hospital-based interventions to more proactive, everyday management in the community,” says Sung, who serves as co‑lead PI of WITEC.

Led by Violet Hoon, senior consultant at TTSH, clinical trials are expected to commence this year to validate long-term heart monitoring in the management of chronic cardiovascular disease. Over the next three years, WITEC aims to develop a fully integrated platform capable of 48-hour intermittent imaging through innovations in bioadhesive couplants, nanostructured metamaterials, and ultrasonic transducers.

As MIT’s research enterprise in Singapore, SMART is committed to advancing breakthrough technologies that address pressing global challenges. WITEC adds to SMART’s existing research endeavors that foster a rich exchange of ideas through collaboration with leading researchers and academics from the United States, Singapore, and around the world in key areas such as antimicrobial resistance, cell therapy development, precision agriculture, AI, and 3D-sensing technologies.

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New tissue models could help researchers develop drugs for liver disease

More than 100 million people in the United States suffer from metabolic dysfunction-associated steatotic liver disease (MASLD), characterized by a buildup of fat in the liver. This condition can lead to the development of more severe liver disease that causes inflammation and fibrosis.

In hopes of discovering new treatments for these liver diseases, MIT engineers have designed a new type of tissue model that more accurately mimics the architecture of the liver, including blood vessels and immune cells.

Reporting their findings today in Nature Communications, the researchers showed that this model could accurately replicate the inflammation and metabolic dysfunction that occur in the early stages of liver disease. Such a device could help researchers identify and test new drugs to treat those conditions.

This is the latest study in a larger effort by this team to use these types of tissue models, also known as microphysiological systems, to explore human liver biology, which cannot be easily replicated in mice or other animals.

In another recent paper, the researchers used an earlier version of their liver tissue model to explore how the liver responds to resmetirom. This drug is used to treat an advanced form of liver disease called metabolic dysfunction-associated steatohepatitis (MASH), but it is only effective in about 30 percent of patients. The team found that the drug can induce an inflammatory response in liver tissue, which may help to explain why it doesn’t help all patients.

“There are already tissue models that can make good preclinical predictions of liver toxicity for certain drugs, but we really need to better model disease states, because now we want to identify drug targets, we want to validate targets. We want to look at whether a particular drug may be more useful early or later in the disease,” says Linda Griffith, the School of Engineering Professor of Teaching Innovation at MIT, a professor of biological engineering and mechanical engineering, and the senior author of both studies.

Former MIT postdoc Dominick Hellen is the lead author of the resmetirom paper, which appeared Jan. 14 in Communications Biology. Erin Tevonian PhD ’25 and PhD candidate Ellen Kan, both in the Department of Biological Engineering, are the lead authors of today’s Nature Communications paper on the new microphysiological system.

Modeling drug response

In the Communications Biology paper, Griffith’s lab worked with a microfluidic device that she originally developed in the 1990s, known as the LiverChip. This chip offers a simple scaffold for growing 3D models of liver tissue from hepatocytes, the primary cell type in the liver.

This chip is widely used by pharmaceutical companies to test whether their new drugs have adverse effects on the liver, which is an important step in drug development because most drugs are metabolized by the liver.

For the new study, Griffith and her students modified the chip so that it could be used to study MASLD.

Patients with MASLD, a buildup of fat in the liver, can eventually develop MASH, a more severe disease that occurs when scar tissue called fibrosis forms in the liver. Currently, resmetirom and the GLP-1 drug semaglutide are the only medications that are FDA-approved to treat MASH. Finding new drugs is a priority, Griffith says.

“You’re never declaring victory with liver disease with one drug or one class of drugs, because over the long term there may be patients who can’t use them, or they may not be effective for all patients,” she says.

To create a model of MASLD, the researchers exposed the tissue to high levels of insulin, along with large quantities of glucose and fatty acids. This led to a buildup of fatty tissue and the development of insulin resistance, a trait that is often seen in MASLD patients and can lead to type 2 diabetes.

Once that model was established, the researchers treated the tissue with resmetirom, a drug that works by mimicking the effects of thyroid hormone, which stimulates the breakdown of fat.

To their surprise, the researchers found that this treatment could also lead to an increase in immune signaling and markers of inflammation.

“Because resmetirom is primarily intended to reduce hepatic fibrosis in MASH, we found the result quite paradoxical,” Hellen says. “We suspect this finding may help clinicians and scientists alike understand why only a subset of patients respond positively to the thyromimetic drug. However, additional experiments are needed to further elucidate the underlying mechanism.”

A more realistic liver model

In the Nature Communications paper, the researchers reported a new type of chip that allows them to more accurately reproduce the architecture of the human liver. The key advance was developing a way to induce blood vessels to grow into the tissue. These vessels can deliver nutrients and also allow immune cells to flow through the tissue.

“Making more sophisticated models of liver that incorporate features of vascularity and immune cell trafficking that can be maintained over a long time in culture is very valuable,” Griffith says. “The real advance here was showing that we could get an intimate microvascular network through liver tissue and that we could circulate immune cells. This helped us to establish differences between how immune cells interact with the liver cells in a type two diabetes state and a healthy state.”

As the liver tissue matured, the researchers induced insulin resistance by exposing the tissue to increased levels of insulin, glucose, and fatty acids.

As this disease state developed, the researchers observed changes in how hepatocytes clear insulin and metabolize glucose, as well as narrower, leakier blood vessels that reflect microvascular complications often seen in diabetic patients. They also found that insulin resistance leads to an increase in markers of inflammation that attract monocytes into the tissue. Monocytes are the precursors of macrophages, immune cells that help with tissue repair during inflammation and are also observed in the liver of patients with early-stage liver disease.

“This really shows that we can model the immune features of a disease like MASLD, in a way that is all based on human cells,” Griffith says.

The research was funded by the National Institutes of Health, the National Science Foundation Graduate Research Fellowship program, NovoNordisk, the Massachusetts Life Sciences Center, and the Siebel Scholars Foundation.

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Young and gifted

James Baldwin was a prodigy. That is not the first thing most people associate with a writer who once declared that he “had no childhood” and whose work often elides the details of his early life in New York, in the 1920s and 1930s. Still, by the time Baldwin was 14, he was a successful church preacher, excelling in a role otherwise occupied by adults.

Throw in the fact that Baldwin was reading Dostoyevsky by the fifth grade, wrote “like an angel” according to his elementary school principal, edited his middle school periodical, and wrote for his high school magazine, and it’s clear he was a precocious wordsmith.

These matters are complicated, of course. To MIT scholar Joshua Bennett, Baldwin’s writings reveal enough for us to conclude that his childhood was marked by a “relentless introspection” as he sought to come to terms with the world. Beyond that, Bennett thinks, some of Baldwin’s work, and even the one children’s book he wrote, yields “messages of persistence,” recognizing the need for any child to receive encouragement and education.

And if someone as precocious as Baldwin still needed cultivation, then virtually everyone does. If we act is if talent blossoms on its own, we are ignoring the vital role communities, teachers, and families play in helping artists — or anyone — develop their skills.

“We talk as if these people emerged ex nihilo,” Bennett says. “When all along the way, there were people who cultivated them, and our children deserve the same — all of the children of the world. We have a dominant model of genius that is fundamentally flawed, in that it often elides the role of communities and cultural institutions.”

Bennett explores these issues in a new book, “The People Can Fly: American Promise, Black Prodigies, and the Greatest Miracle of All Time,” published this week by Hachette. A literary scholar and poet himself, Bennett is the Distinguished Chair of the Humanities at MIT and a professor of literature.

“The People Can Fly” accomplishes many kinds of work at once: Bennett offers a series of profiles, carefully wrought to see how some prominent figures were able to flourish from childhood forward. And he closely reads their works for indications about how they understood the shape of their own lives. In so doing, Bennett underscores the significance of the social settings that prodigious talents grow up in. For good measure, he also offers reflections on his own career trajectory and encounters with these artists, driving home their influence and meaning.

Reading about these many prodigies, one by one, helps readers build a picture of the realities, and complications, of trying to sustain early promise.

“It’s part of what I tell my students — the individual is how you get to the universal,” Bennett says. “It doesn’t mean I need to share certain autobiographical impulses with, say, Hemingway. It’s just that I think those touchpoints exist in all great works of art.”

Space odyssey

For Bennett, the idea of writing about prodigies grew naturally from his research and teaching, which ranges broadly in American and global literature. Bennett began contemplating “the idea of promise as this strange, idiosyncratic quality, this thing we see through various acts, perhaps something as simple as a little riff you hear a child sing, an element of their drawings, or poems.” At the same time, he notes, people struggle with “the weight of promise. There is a peril that can come along with promise. Promise can be taken away.”

Ultimately, Bennett adds, “I started thinking a little more about what promise has meant in African American communities,” in particular. Ranging widely in the book, Bennett consistently loops back to a core focus on the ideals, communities, and obstacles many Black artists grew up with. These artists and intellectuals include Malcolm X, Gwendolyn Brooks, Stevie Wonder, and the late poet and scholar Nikki Giovanni.

Bennett’s chapter on Giovanni shows his own interest in placing an artist’s life in historical context, and picks up on motifs relating back to childhood and personal promise.

Giovanni attended Fisk University early, enrolling at 17. Later she enrolled in Columbia University’s Masters of Fine Arts program, where poetry students were supposed to produce publishable work in a two-year program. In her first year, Giovanni’s poetry collection, “Black Feeling, Black Talk,” not only got published but became a hit, selling 10,000 copies. She left the program early — without a degree, since it required two years of residency. In short, she was always going places.

Giovanni went on to become one of the most celebrated poets of her time, and spent decades on the faculty at Virginia Tech. One idea that kept recurring in her work: dreams of space exploration. Giovanni’s work transmitted a clear enthusiasm for exploring the stars.

“Looking through her work, you see space travel everywhere,” Bennett says. “Even in her most prominent poem, ‘Ego trippin (there may be a reason why),’ there is this sense of someone who’s soaring over the landscape — ‘I’m so hip even my errors are correct.’ There is this idea of an almost divine being.”

That enthusiasm was accompanied by the recognition that astronauts, at least at one time, emerged from a particular slice of society. Indeed, Giovanni at many times publicly called for more opportunities for more Americans to become astronauts. A pressing issue, for her, was making dreams achievable for more people.

“Nikki Giovanni is very invested in these sorts of questions, as a writer, as an educator, and as a big thinker,” Bennett says. “This kind of thinking about the cosmos is everywhere in her work. But inside of that is a critique, that everyone should have a chance to expand the orbit of their dreaming. And dream of whatever they need to.”

And as Bennett draws out in “The People Can Fly,” stories and visions of flying have run deep in Black culture, offering a potent symbolism and a mode of “holding on to a deeper sense that the constraints of this present world are not all-powerful or everlasting. The miraculous is yet available. The people could fly, and still can.”

Children with promise, families with dreams

Other artists have praised “The People Can Fly.” The actor, producer, and screenwriter Lena Waithe has said that “Bennett’s poetic nature shines through on every page. … This book is a masterclass in literature and a necessary reminder to cherish the child in all of us.”

Certainly Bennett brings a vast sense of scope to “The People Can Fly,” ranging across centuries of history. Phillis Wheatley, a former enslaved woman whose 1773 poetry collection was later praised by George Washington, was an early American prodigy, studying the classics as a teenager and releasing her work at age 20. Mae Jemison, the first Black female astronaut, enrolled in Stanford University at age 16, spurred by family members who taught her about the stars. All told, Bennett weaves together a scholarly tapestry about hope, ambition, and, at times, opportunity.

Often, that hope and ambition belong to whole families, not just one gifted child. As Nikki Giovanni herself quipped, while giving the main address at MIT’s annual Martin Luther King convocation in 1990, “the reason you go to college is that it makes your mother happy.”

Bennett can relate, having come from a family where his mother was the only prior relative to have attended college. As a kid in the 1990s, growing up in Yonkers, New York, he had a Princeton University sweatshirt, inspired by his love of the television program “The Fresh Prince of Bel Air.” The program featured a character named Phillip Banks — popularly known as “Uncle Phil” — who was, within the world of the show, a Princeton alumnus.

“I would ask my Mom, ‘How do I get into Princeton?’” Bennett recalls. “She would just say, ‘Study hard, honey.’ No one but her had even been to college in my family. No one had been to Princeton. No one had set foot on Princeton University’s campus. But the idea that was possible in the country we lived in, for a woman who was the daughter of two sharecroppers, and herself grew up in a tenement with her brothers and sister, and nonetheless went on to play at Carnegie Hall and get a college degree and buy her mother a color TV — it’s fascinating to me.”

The postscript to that anecdote is that Bennett did go on to earn his PhD from Princeton. Behind many children with promise are families and communities with dreams for those kids.

“There’s something to it I refuse to relinquish,” Bennett says. “My mother’s vision was a powerful and persistent one — she believed that the future also belonged to her children.”

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How a unique class of neurons may set the table for brain development

The way the brain develops can shape us throughout our lives, so neuroscientists are intensely curious about how it happens. A new study by researchers in The Picower Institute for Learning and Memory at MIT that focused on visual cortex development in mice reveals that an important class of neurons follows a set of rules that, while surprising, might just create the right conditions for circuit optimization.

During early brain development, multiple types of neurons emerge in the visual cortex (where the brain processes vision). Many are “excitatory,” driving the activity of brain circuits, and others are “inhibitory,” meaning they control that activity. Just like a car needs not only an engine and a gas pedal, but also a steering wheel and brakes, a healthy balance between excitation and inhibition is required for proper brain function. During a “critical period” of development in the visual cortex, soon after the eyes first open, excitatory and inhibitory neurons forge and edit millions of connections, or synapses, to adapt nascent circuits to the incoming flood of visual experience. Over many days, in other words, the brain optimizes its attunement to the world.

In the new study in The Journal of Neuroscience, a team led by MIT research scientist Josiah Boivin and Professor Elly Nedivi visually tracked somatostatin (SST)-expressing inhibitory neurons forging synapses with excitatory cells along their sprawling dendrite branches, illustrating the action before, during, and after the critical period with unprecedented resolution. Several of the rules the SST cells appeared to follow were unexpected — for instance, unlike other cell types, their activity did not depend on visual input — but now that the scientists know these neurons’ unique trajectory, they have a new idea about how it may enable sensory activity to influence development: SST cells might help usher in the critical period by establishing the baseline level of inhibition needed to ensure that only certain types of sensory input will trigger circuit refinement.

“Why would you need part of the circuit that’s not really sensitive to experience? It could be that it’s setting things up for the experience-dependent components to do their thing,” says Nedivi, the William R. and Linda R. Young Professor in the Picower Institute and MIT’s departments of Biology and Brain and Cognitive Sciences.

Boivin adds: “We don’t yet know whether SST neurons play a causal role in the opening of the critical period, but they are certainly in the right place at the right time to sculpt cortical circuitry at a crucial developmental stage.”

A unique trajectory

To visualize SST-to-excitatory synapse development, Nedivi and Boivin’s team used a genetic technique that pairs expression of synaptic proteins with fluorescent molecules to resolve the appearance of the “boutons” SST cells use to reach out to excitatory neurons. They then performed a technique called eMAP, developed by Kwanghun Chung’s lab in the Picower Institute, that expands and clears brain tissue to increase magnification, allowing super-resolution visualization of the actual synapses those boutons ultimately formed with excitatory cells along their dendrites. Co-author and postdoc Bettina Schmerl helped lead the eMAP work.

These new techniques revealed that SST bouton appearance and then synapse formation surged dramatically when the eyes opened, and then as the critical period got underway. But while excitatory neurons during this time frame are still maturing, first in the deepest layers of the cortex and later in its more superficial layers, the SST boutons blanketed all layers simultaneously, meaning that, perhaps counterintuitively, they sought to establish their inhibitory influence regardless of the maturation stage of their intended partners.

Many studies have shown that eye opening and the onset of visual experience sets in motion the development and elaboration of excitatory cells and another major inhibitory neuron type (parvalbumin-expressing cells). Raising mice in the dark for different lengths of time, for instance, can distinctly alter what happens with these cells. Not so for the SST neurons. The new study showed that varying lengths of darkness had no effect on the trajectory of SST bouton and synapse appearance; it remained invariant, suggesting it is preordained by a genetic program or an age-related molecular signal, rather than experience.

Moreover, after the initial frenzy of synapse formation during development, many synapses are then edited, or pruned away, so that only the ones needed for appropriate sensory responses endure. Again, the SST boutons and synapses proved to be exempt from these redactions. Although the pace of new SST synapse formation slowed at the peak of the critical period, the net number of synapses never declined, and even continued increasing into adulthood.

“While a lot of people think that the only difference between inhibition and excitation is their valence, this demonstrates that inhibition works by a totally different set of rules,” Nedivi says.

In all, while other cell types were tailoring their synaptic populations to incoming experience, the SST neurons appeared to provide an early but steady inhibitory influence across all layers of the cortex. After excitatory synapses have been pruned back by the time of adulthood, the continued upward trickle of SST inhibition may contribute to the increase in the inhibition to excitation ratio that still allows the adult brain to learn, but not as dramatically or as flexibly as during early childhood.

A platform for future studies

In addition to shedding light on typical brain development, Nedivi says, the study’s techniques can enable side-by-side comparisons in mouse models of neurodevelopmental disorders such as autism or epilepsy, where aberrations of excitation and inhibition balance are implicated.

Future studies using the techniques can also look at how different cell types connect with each other in brain regions other than the visual cortex, she adds.

Boivin, who will soon open his own lab as a faculty member at Amherst College, says he is eager to apply the work in new ways.

“I’m excited to continue investigating inhibitory synapse formation on genetically defined cell types in my future lab,” Boivin says. “I plan to focus on the development of limbic brain regions that regulate behaviors relevant to adolescent mental health.”

In addition to Nedivi, Boivin and Schmerl, the paper’s other authors are Kendyll Martin and Chia-Fang Lee.

Funding for the study came from the National Institutes of Health, the Office of Naval Research, and the Freedom Together Foundation.

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How generative AI can help scientists synthesize complex materials

Generative artificial intelligence models have been used to create enormous libraries of theoretical materials that could help solve all kinds of problems. Now, scientists just have to figure out how to make them.

In many cases, materials synthesis is not as simple as following a recipe in the kitchen. Factors like the temperature and length of processing can yield huge changes in a material’s properties that make or break its performance. That has limited researchers’ ability to test millions of promising model-generated materials.

Now, MIT researchers have created an AI model that guides scientists through the process of making materials by suggesting promising synthesis routes. In a new paper, they showed the model delivers state-of-the-art accuracy in predicting effective synthesis pathways for a class of materials called zeolites, which could be used to improve catalysis, absorption, and ion exchange processes. Following its suggestions, the team synthesized a new zeolite material that showed improved thermal stability.

The researchers believe their new model could break the biggest bottleneck in the materials discovery process.

“To use an analogy, we know what kind of cake we want to make, but right now we don’t know how to bake the cake,” says lead author Elton Pan, a PhD candidate in MIT’s Department of Materials Science and Engineering (DMSE). “Materials synthesis is currently done through domain expertise and trial and error.”

The paper describing the work appears today in Nature Computational Science. Joining Pan on the paper are Soonhyoung Kwon ’20, PhD ’24; DMSE postdoc Sulin Liu; chemical engineering PhD student Mingrou Xie; DMSE postdoc Alexander J. Hoffman; Research Assistant Yifei Duan SM ’25; DMSE visiting student Thorben Prein; DMSE PhD candidate Killian Sheriff; MIT Robert T. Haslam Professor in Chemical Engineering Yuriy Roman-Leshkov; Valencia Polytechnic University Professor Manuel Moliner; MIT Paul M. Cook Career Development Professor Rafael Gómez-Bombarelli; and MIT Jerry McAfee Professor in Engineering Elsa Olivetti.

Learning to bake

Massive investments in generative AI have led companies like Google and Meta to create huge databases filled with material recipes that, at least theoretically, have properties like high thermal stability and selective absorption of gases. But making those materials can require weeks or months of careful experiments that test specific reaction temperatures, times, precursor ratios, and other factors.

“People rely on their chemical intuition to guide the process,” Pan says. “Humans are linear. If there are five parameters, we might keep four of them constant and vary one of them linearly. But machines are much better at reasoning in a high-dimensional space.”

The synthesis process of materials discovery now often takes the most time in a material’s journey from hypothesis to use.

To help scientists navigate that process, the MIT researchers trained a generative AI model on over 23,000 material synthesis recipes described over 50 years of scientific papers. The researchers iteratively added random “noise” to the recipes during training, and the model learned to de-noise and sample from the random noise to find promising synthesis routes.

The result is DiffSyn, which uses an approach in AI known as diffusion.

“Diffusion models are basically a generative AI model like ChatGPT, but more like the DALL-E image generation model,” Pan says. “During inference, it converts noise into meaningful structure by subtracting a little bit of noise at each step. In this case, the ‘structure’ is the synthesis route for a desired material.”

When a scientist using DiffSyn enters a desired material structure, the model offers some promising combinations of reaction temperatures, reaction times, precursor ratios, and more.

“It basically tells you how to bake your cake,” Pan says. “You have a cake in mind, you feed it into the model, the model spits out the synthesis recipes. The scientist can pick whichever synthesis path they want, and there are simple ways to quantify the most promising synthesis path from what we provide, which we show in our paper.”

To test their system, the researchers used DiffSyn to suggest novel synthesis paths for a zeolite, a material class that is complex and takes time to form into a testable material.

“Zeolites have a very high-dimensional synthesis space,” Pan says. “Zeolites also tend to take days or weeks to crystallize, so the impact [of finding the best synthesis pathway faster] is much higher than other materials that crystallize in hours.”

The researchers were able to make the new zeolite material using synthesis pathways suggested by DiffSyn. Subsequent testing revealed the material had a promising morphology for catalytic applications.

“Scientists have been trying out different synthesis recipes one by one,” Pan says. “That makes them very time-consuming. This model can sample 1,000 of them in under a minute. It gives you a very good initial guess on synthesis recipes for completely new materials.”

Accounting for complexity

Previously, researchers have built machine-learning models that mapped a material to a single recipe. Those approaches do not take into account that there are different ways to make the same material.

DiffSyn is trained to map material structures to many different possible synthesis paths. Pan says that is better aligned with experimental reality.

“This is a paradigm shift away from one-to-one mapping between structure and synthesis to one-to-many mapping,” Pan says. “That’s a big reason why we achieved strong gains on the benchmarks.”

Moving forward, the researchers believe the approach should work to train other models that guide the synthesis of materials outside of zeolites, including metal-organic frameworks, inorganic solids, and other materials that have more than one possible synthesis pathway.

“This approach could be extended to other materials,” Pan says. “Now, the bottleneck is finding high-quality data for different material classes. But zeolites are complicated, so I can imagine they are close to the upper-bound of difficulty. Eventually, the goal would be interfacing these intelligent systems with autonomous real-world experiments, and agentic reasoning on experimental feedback to dramatically accelerate the process of materials design.”

The work was supported by MIT International Science and Technology Initiatives (MISTI), the National Science Foundation, Generalitat Vaslenciana, the Office of Naval Research, ExxonMobil, and the Agency for Science, Technology and Research in Singapore.

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A portable ultrasound sensor may enable earlier detection of breast cancer

For people who are at high risk of developing breast cancer, frequent screenings with ultrasound can help detect tumors early. MIT researchers have now developed a miniaturized ultrasound system that could make it easier for breast ultrasounds to be performed more often, either at home or at a doctor’s office.

The new system consists of a small ultrasound probe attached to an acquisition and processing module that is a little larger than a smartphone. This system can be used on the go when connected to a laptop computer to reconstruct and view wide-angle 3D images in real-time.

“Everything is more compact, and that can make it easier to be used in rural areas or for people who may have barriers to this kind of technology,” says Canan Dagdeviren, an associate professor of media arts and sciences at MIT and the senior author of the study.

With this system, she says, more tumors could potentially be detected earlier, which increases the chances of successful treatment.

Colin Marcus PhD ’25 and former MIT postdoc Md Osman Goni Nayeem are the lead authors of the paper, which appears in the journal Advanced Healthcare Materials. Other authors of the paper are MIT graduate students Aastha Shah, Jason Hou, and Shrihari Viswanath; MIT summer intern and University of Central Florida undergraduate Maya Eusebio; MIT Media Lab Research Specialist David Sadat; MIT Provost Anantha Chandrakasan; and Massachusetts General Hospital breast cancer surgeon Tolga Ozmen.

Frequent monitoring

While many breast tumors are detected through routine mammograms, which use X-rays, tumors can develop in between yearly mammograms. These tumors, known as interval cancers, account for 20 to 30 percent of all breast cancer cases, and they tend to be more aggressive than those found during routine scans.

Detecting these tumors early is critical: When breast cancer is diagnosed in the earliest stages, the survival rate is nearly 100 percent. However, for tumors detected in later stages, that rate drops to around 25 percent.

For some individuals, more frequent ultrasound scanning in addition to regular mammograms could help to boost the number of tumors that are detected early. Currently, ultrasound is usually done only as a follow-up if a mammogram reveals any areas of concern. Ultrasound machines used for this purpose are large and expensive, and they require highly trained technicians to use them.

“You need skilled ultrasound technicians to use those machines, which is a major obstacle to getting ultrasound access to rural communities, or to developing countries where there aren’t as many skilled radiologists,” Viswanath says.

By creating ultrasound systems that are portable and easier to use, the MIT team hopes to make frequent ultrasound scanning accessible to many more people.

In 2023, Dagdeviren and her colleagues developed an array of ultrasound transducers that were incorporated into a flexible patch that can be attached to a bra, allowing the wearer to move an ultrasound tracker along the patch and image the breast tissue from different angles.

Those 2D images could be combined to generate a 3D representation of the tissue, but there could be small gaps in coverage, making it possible that small abnormalities could be missed. Also, that array of transducers had to be connected to a traditional, costly, refrigerator-sized processing machine to view the images.

In their new study, the researchers set out to develop a modified ultrasound array that would be fully portable and could create a 3D image of the entire breast by scanning just two or three locations.

The new system they developed is a chirped data acquisition system (cDAQ) that consists of an ultrasound probe and a motherboard that processes the data. The probe, which is a little smaller than a deck of cards, contains an ultrasound array arranged in the shape of an empty square, a configuration that allows the array to take 3D images of the tissue below.

This data is processed by the motherboard, which is a little bit larger than a smartphone and costs only about $300 to make. All of the electronics used in the motherboard are commercially available. To view the images, the motherboard can be connected to a laptop computer, so the entire system is portable.

“Traditional 3D ultrasound systems require power expensive and bulky electronics, which limits their use to high-end hospitals and clinics,” Chandrakasan says. “By redesigning the system to be ultra-sparse and energy-efficient, this powerful diagnostic tool can be moved out of the imaging suite and into a wearable form factor that is accessible for patients everywhere.”

This system also uses much less power than a traditional ultrasound machine, so it can be powered with a 5V DC supply (a battery or an AC/DC adapter used to plug in small electronic devices such as modems or portable speakers).

“Ultrasound imaging has long been confined to hospitals,” says Nayeem. “To move ultrasound beyond the hospital setting, we reengineered the entire architecture, introducing a new ultrasound fabrication process, to make the technology both scalable and practical.”

Earlier diagnosis

The researchers tested the new system on one human subject, a 71-year-old woman with a history of breast cysts. They found that the system could accurately image the cysts and created a 3D image of the tissue, with no gaps.

The system can image as deep as 15 centimeters into the tissue, and it can image the entire breast from two or three locations. And, because the ultrasound device sits on top of the skin without having to be pressed into the tissue like a typical ultrasound probe, the images are not distorted.

“With our technology, you simply place it gently on top of the tissue and it can visualize the cysts in their original location and with their original sizes,” Dagdeviren says.

The research team is now conducting a larger clinical trial at the MIT Center for Clinical and Translational Research and at MGH.

The researchers are also working on an even smaller version of the data processing system, which will be about the size of a fingernail. They hope to connect this to a smartphone that could be used to visualize the images, making the entire system smaller and easier to use. They also plan to develop a smartphone app that would use an AI algorithm to help guide the patient to the best location to place the ultrasound probe.

While the current version of the device could be readily adapted for use in a doctor’s office, the researchers hope that the future, a smaller version can be incorporated into a wearable sensor that could be used at home by people at high risk for developing breast cancer.

Dagdeviren is now working on launching a company to help commercialize the technology, with assistance from an MIT HEALS Deshpande Momentum Grant, the Martin Trust Center for MIT Entrepreneurship, and the MIT Media Lab WHx Women’s Health Innovation Fund.

The research was funded by a National Science Foundation CAREER Award, a 3M Non-Tenured Faculty Award, the Lyda Hill Foundation, and the MIT Media Lab Consortium.

de MIT News https://ift.tt/V9iJa0C

The philosophical puzzle of rational artificial intelligence

To what extent can an artificial system be rational?

A new MIT course, 6.S044/24.S00 (AI and Rationality), doesn’t seek to answer this question. Instead, it challenges students to explore this and other philosophical problems through the lens of AI research. For the next generation of scholars, concepts of rationality and agency could prove integral in AI decision-making, especially when influenced by how humans understand their own cognitive limits and their constrained, subjective views of what is or isn’t rational.

This inquiry is rooted in a deep relationship between computer science and philosophy, which have long collaborated in formalizing what it is to form rational beliefs, learn from experience, and make rational decisions in pursuit of one's goals.

“You’d imagine computer science and philosophy are pretty far apart, but they’ve always intersected. The technical parts of philosophy really overlap with AI, especially early AI,” says course instructor Leslie Kaelbling, the Panasonic Professor of Computer Science and Engineering at MIT, calling to mind Alan Turing, who was both a computer scientist and a philosopher. Kaelbling herself holds an undergraduate degree in philosophy from Stanford University, noting that computer science wasn’t available as a major at the time.

Brian Hedden, a professor in the Department of Linguistics and Philosophy, holding an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science (EECS), who teaches the class with Kaelbling, notes that the two disciplines are more aligned than people might imagine, adding that the “differences are in emphasis and perspective.”

Tools for further theoretical thinking

Offered for the first time in fall 2025, Kaelbling and Hedden created AI and Rationality as part of the Common Ground for Computing Education, a cross-cutting initiative of the MIT Schwarzman College of Computing that brings multiple departments together to develop and teach new courses and launch new programs that blend computing with other disciplines.

With over two dozen students registered, AI and Rationality is one of two Common Ground classes with a foundation in philosophy, the other being 6.C40/24.C40 (Ethics of Computing).

While Ethics of Computing explores concerns about the societal impacts of rapidly advancing technology, AI and Rationality examines the disputed definition of rationality by considering several components: the nature of rational agency, the concept of a fully autonomous and intelligent agent, and the ascription of beliefs and desires onto these systems.

Because AI is extremely broad in its implementation and each use case raises different issues, Kaelbling and Hedden brainstormed topics that could provide fruitful discussion and engagement between the two perspectives of computer science and philosophy.

“It's important when I work with students studying machine learning or robotics that they step back a bit and examine the assumptions they’re making,” Kaelbling says. “Thinking about things from a philosophical perspective helps people back up and understand better how to situate their work in actual context.”

Both instructors stress that this isn’t a course that provides concrete answers to questions on what it means to engineer a rational agent.

Hedden says, “I see the course as building their foundations. We’re not giving them a body of doctrine to learn and memorize and then apply. We’re equipping them with tools to think about things in a critical way as they go out into their chosen careers, whether they’re in research or industry or government.”

The rapid progress of AI also presents a new set of challenges in academia. Predicting what students may need to know five years from now is something Kaelbling sees as an impossible task. “What we need to do is give them the tools at a higher level — the habits of mind, the ways of thinking — that will help them approach the stuff that we really can’t anticipate right now,” she says.

Blending disciplines and questioning assumptions

So far, the class has drawn students from a wide range of disciplines — from those firmly grounded in computing to others interested in exploring how AI intersects with their own fields of study.

Throughout the semester’s reading and discussions, students grappled with different definitions of rationality and how they pushed back against assumptions in their fields.

On what surprised her about the course, Amanda Paredes Rioboo, a senior in EECS, says, “We’re kind of taught that math and logic are this golden standard or truth. This class showed us a variety of examples that humans act inconsistently with these mathematical and logical frameworks. We opened up this whole can of worms as to whether, is it humans that are irrational? Is it the machine learning systems that we designed that are irrational? Is it math and logic itself?”

Junior Okoroafor, a PhD student in the Department of Brain and Cognitive Sciences, was appreciative of the class’s challenges and the ways in which the definition of a rational agent could change depending on the discipline. “Representing what each field means by rationality in a formal framework, makes it clear exactly which assumptions are to be shared, and which were different, across fields.”

The co-teaching, collaborative structure of the course, as with all Common Ground endeavors, gave students and the instructors opportunities to hear different perspectives in real-time.

For Paredes Rioboo, this is her third Common Ground course. She says, “I really like the interdisciplinary aspect. They’ve always felt like a nice mix of theoretical and applied from the fact that they need to cut across fields.”

According to Okoroafor, Kaelbling and Hedden demonstrated an obvious synergy between fields, saying that it felt as if they were engaging and learning along with the class. How computer science and philosophy can be used to inform each other allowed him to understand their commonality and invaluable perspectives on intersecting issues.

He adds, “philosophy also has a way of surprising you.”

de MIT News https://ift.tt/Vtinbal