2026-03-22 21:00:04
Most people who regularly use AI tools would say they’re making their lives easier. The technology promises to streamline and take over tasks both professionally and personally—whether that’s summarizing documents, drafting deliverables, generating code, or even offering emotional support. But researchers are concerned AI is making some tasks too easy, and that this will come with unexpected costs.
In a commentary titled Against Frictionless AI, published in Communications Psychology on 24 February, psychologists from the University of Toronto discuss what might be lost when AI removes too much effort from human activities. Their argument centers on the idea that friction—difficulty, struggle, and even discomfort—plays an important role in learning, motivation, and meaning. Psychological research has long shown that effortful engagement can deepen understanding and strengthen memory, sometimes described as “desirable difficulties.”
The authors worry that AI systems capable of instantly producing polished answers or highly responsive conversation may bypass these processes of learning and motivation. By prioritizing outcomes over effort, AI could weaken the experiences that help people develop skills, build relationships, and find meaning in their work.
IEEE Spectrum spoke with the paper’s lead author, Emily Zohar, an experimental psychology Ph.D. student, about why she and her coauthors (psychologists Paul Bloom and Michael Inzlicht) argue that friction matters—and what a more human-centered approach to AI design could look like.
When you say “friction,” what do you mean, from both a cognitive and an interpersonal standpoint?
Zohar: We define friction as any difficulty encountered during goal pursuit. In the context of work, it involves mental effort—rumination and persistence, staying on a problem for some time, and this helps solidify the idea and the creative process.
In relationships, friction involves disagreement, compromise, misunderstanding, a back and forth that is natural where you don’t always see eye to eye, and it helps you broaden your horizons. Even the feeling of loneliness is important. It motivates you to find social interactions. So having these negative feelings and difficulty is important in the social context.
Given that definition, what do you mean by “frictionless” AI?
Zohar: Frictionless AI refers to the excessive removal of effort from cognitive and social tasks. With AI, as we typically use it, it’s really easy to go from ideation right to the end product. You ask AI to solve something with one prompt, and it completes the whole thing. This is a problem because it takes away the intermediate steps that really drive motivation and learning, and it prioritizes outcome over process. Rather than working through the steps, AI does that meaningful work for you.
There’s a lot of research showing work products are better with AI. That makes sense, it has all this knowledge, but it does worry us as it may be eroding something essential that will have long-term consequences. If you’re faced with the same problem and AI is removed, you don’t have the required knowledge to know how to face the problem next time.
You argue that removing friction can harm learning and relationships. What role do effort and struggle play in human development?
Zohar: In learning, the term is “desirable difficulties.” It’s the idea of effort and work, not just any effort but manageable effort. Facing problems that you can overcome, but you have to work at them a bit, that’s the key idea of friction. We don’t want you to face insurmountable problems. We want you to work hard, but still be able to overcome it. This helps you really digest information and learn from it.
In interpersonal relationships, you have to face some difficulties to see other perspectives and learn from them, and learn to be accepting of others. If you’re used to an AI reinforcing all your ideas and being sycophantic, you’ll come into the real world and you won’t be used to seeing other ideas. You won’t know how to interact socially because you’ll expect people to always be on your side and agree with you. You won’t learn that life doesn’t always go exactly how you expect it to, and conversations don’t always go the way you want them to.
A lot of technologies have historically aimed to reduce effort: calculators, washing machines, spellcheck. What’s different about AI?
Zohar: Past technologies have mostly focused on reducing physical effort. We don’t have to go down to the lake to wash our laundry anymore. [Past technologies] took away the mundane tasks that weren’t driving our learning and growth, they were just adding unneeded obstacles and taking away time from more important tasks.
But AI is taking away effort from creative and cognitive processes that drive meaning, motivation, and learning. That’s a key difference, because it’s not taking away friction from tasks that don’t serve us. It’s taking away friction from experiences that are really important and integral to our development.
Are there contexts where AI is already removing beneficial friction? How might the impacts of reduced friction show up over time?
Zohar: One clear example is writing. People increasingly rely on AI to draft everything from emails to essays, removing many instances of beneficial friction. Research shows that people trust responses less when they learn they were written by AI, judge AI-generated products as less creative and less valuable, and have greater difficulty remembering their own work products when they were produced with AI assistance. Outsourcing writing to AI strips away both social and cognitive friction.
Vibe coding is another good example. If you’re a programmer, coding is integral to what drives your meaning. People get meaning out of their work, and if you’re substituting that with AI, it could be detrimental. The negative impact of frictionless AI is that it takes away friction from things that are really important to who you are as a person, and your skills.
One area I worry about a lot is adolescents using AI in general. It’s a really important developmental period to learn and grow and find the path you’ll follow. So if you don’t have these effortful interactions with work and relationships that teach you how to think, this will have long-term detrimental impacts. They might not be able to think critically in the same way, because they never had to before. If they’re turning to AI for social relationships at such a young age, that could really erode important skills they should be learning at that age.
What is productive friction?
Zohar: Friction goes along a continuum. With too little friction, you’re not getting learning and motivation. Too much friction and the task becomes overwhelming. Productive friction falls right in the middle, where struggle leads to achievement. It’s effortful but possible, and it requires you to think critically and work on a problem for some time or face some difficulty in the process.
An example we used in the paper is the difference between taking a chairlift and hiking up a mountain. They both get to the top, but with the chairlift, you don’t get any growth benefits, while the hiker’s climb involves difficulties and a sense of achievement. It becomes much more of an experience and a learning opportunity versus the person who just went up the chairlift effortlessly.
Do you envision AI that sometimes deliberately slows people down or asks them to do part of the work themselves?
Zohar: It’s important in behavioral science to think about the default option, because people don’t usually change their default. So right now, the default in AI is to give you your answer and probe you to keep going down the rabbit hole. But I think we could think about AI in a different way. Maybe we can make the default more constructive. Instead of just jumping to the answer, it’s more of a process model where it helps you think about the problem and teaches you along the way, so it’s more collaborative rather than a one-stop shop for the answer.
How might users of these systems and the companies developing them feel about such a design shift?
Zohar: For the makers of these systems, the biggest concern is the pushback. People are used to going in and just getting the answer, and they might be really resistant to a design that makes them work more for it. But it might feed more engagement, because you have to go back and forth and find the answer together.
Ultimately I think it has to come from the companies making these models, if they think [a more friction-full design] would help people. Friction-full AI is more of a long-term product. It’s hard to say if that would motivate companies to change their models to include moderate friction. But in the long term, I think this would be beneficial.
2026-03-22 00:30:04
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.
Enjoy today’s videos!
Human athletes demonstrate versatile and highly dynamic tennis skills to successfully conduct competitive rallies with a high-speed tennis ball. However, reproducing such behaviors on humanoid robots is difficult, partially due to the lack of perfect humanoid action data or human kinematic motion data in tennis scenarios as reference. In this work, we propose LATENT, a system that Learns Athletic humanoid TEnnis skills from imperfect human motioN daTa.
[ LATENT ]
A beautifully designed robot inspired by Strandbeests.
We believe we’re the first robotics company to demonstrate a robot peeling an apple with dual dexterous human-like hands. This breakthrough closes a key gap in robotics, achieving bimanual, contact-rich manipulation and moving far beyond the limits of simple grippers.
Today’s AI models (VLMs) are excellent at perception but struggle with action. Controlling high-degree-of-freedom hands for tasks like this is incredibly complex, and precise finger-level teleoperation is nearly impossible for humans. Our first step was a shared-autonomy system: rather than controlling every finger, the operator triggers pre-learned skills like a “rotate apple or tennis ball” primitive via a keyboard press or pedal. This makes scalable data collection and RL training possible.
How does the AI manage this? We created “MoDE-VLA” (Mixture of Dexterous Experts). It fuses vision, language, force, and touch data by using a team of specialist “experts,” making control in high-dimensional spaces stable and effective. The combination of these two innovations allows for seamless, contact-rich manipulation. The human provides high-level guidance, and the robot executes the complex in-hand coordination required.
[ Sharpa ]
Thanks, Alex!
It was great to see our name amongst the other “AI Native” companies during the NVIDIA GTC keynote. NVIDIA Isaac Lab helps us train reinforcement learning policies that enable the UMV to drive, jump, flip, and hop like a pro.
This Finger-Tip Changer technology was jointly researched and developed through a collaboration between Tesollo and RoCogMan LaB at Hanyang University ERICA. The project integrates Tesollo’s practical robotic hand development experience with the lab’s expertise in robotic manipulation and gripper design.
I don’t know why more robots don’t do this. Also, those pointy fingertips are terrifying.
[ RoCogMan LaB ]
Here’s an upcoming ICRA paper from the Fluent Robotics Lab at the University of Michigan featuring an operational PR2! With functional batteries!!!
This video showcases the field tests and interaction capabilities of KAIST Humanoid v0.7, developed at the DRCD Lab featuring in-house actuators. The control policy was trained through deep reinforcement learning leveraging human demonstrations.
[ KAIST DRCD Lab ]
This needs to come in adult size.
[ DEEP Robotics ]
I did not know this, but apparently shoeboxes are really annoying to manipulate because if you grab them by the lid, they just open, so specialized hardware is required.
[ Nomagic ]
Thanks, Gilmarie!
This paper presents a method to recover quadrotor Unmanned Air Vehicles (UAVs) from a throw, when no control parameters are known before the throw.
[ MAVLab ]
Uh oh, robots can see glass doors now. We’re in trouble.
[ LimX Dynamics ]
This drone hugs trees
[ Stanford BDML ]
Electronic waste is one of the fastest-growing environmental problems in the world. As robotics and electronic systems become more widespread, their environmental footprint continues to increase. In this research, scientists developed a fully biodegradable soft robotic system that integrates electronic devices, sensors, and actuators, yet completely decomposes after use.
[ Nature ]
We developed a distributed algorithm that enables multiple aerial robots to flock together safely in complex environments, without explicit communication or prior knowledge of the surroundings, using only on-board sensors and computation. Our approach ensures collision avoidance, maintains proximity between robots, and handles uncertainties (tracking errors and sensor noise). Tested in simulations and real-world experiments with up to four drones in a dense forest, it proved robust and reliable.
[ RBL ]
The University of Pennsylvania’s 2025 President’s Sustainability Prize winner Piotr Lazarek has developed a system that uses satellite data to pinpoint inefficiencies in farmers’ fields, conducts real-time soil analysis with autonomous drones to understand why they occur, and generates precise fertilizer application maps. His startup Nirby aims to increase productivity in farm areas that are underperforming and reduce fertilizer in high-performing ones.
[ University of Pennsylvania ]
The production version of Atlas is a departure from the typical humanoid form factor, favoring industrial utility over human likeness. Intended for purposeful work in an industrial setting, Atlas has a form factor that signals its role as a machine rather than a companion or friendly assistant. Join two lead hardware engineers and our head of industrial design for a technical discussion of how key product requirements, ranging from passive thermal management to a modular architecture, dictated a bold new vision for a humanoid.
[ Boston Dynamics ]
Dr. Christian Hubicki gives a talk exploring the common themes of modern robotics research and his time on the reality competition show, Survivor.
2026-03-21 02:49:12
Wheelchair users with severe disabilities can often navigate tight spaces better than most robotic systems can. A wave of new smart-wheelchair research, including findings presented in Anaheim, Calif., earlier this month, is now testing whether AI-powered systems can, or should, fully close this gap.
Christian Mandel—senior researcher at the German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany—co-led a research team together with his colleague Serge Autexier that developed prototype sensor-equipped electric wheelchairs designed to navigate a roomful of potential obstacles. The researchers also tested a new safety system that integrated sensor data from the wheelchair and from sensors in the room, including from drone-based color and depth cameras.
Mandel says the team’s smart wheelchairs were both semiautonomous and autonomous.
“Semiautonomous is the shared control system where the person sitting in the wheelchair uses the joystick to drive,” Mandel says. “Fully autonomous is controlled by natural-language input. You say, ‘Please drive me to the coffee machine.’ ”
This is a close-up of the wheelchair’s joystick and camera.DFKI
The researchers conducted experiments (part of a larger project called the Reliable and Explainable Swarm Intelligence for People With Reduced Mobility, or REXASI-PRO) using two identical smart wheelchairs that each contained two lidars, a 3D camera, odometers, user interfaces, and an embedded computer.
In contrast to semiautonomous mode, where the participant controls the wheelchair with a joystick, in autonomous mode, control involves the open-source ROS2 Nav2 navigation system using natural-language input. The wheelchairs also used simultaneous localization and mapping (SLAM) maps and local obstacle-avoidance motion controllers.
One scenario that Mandel and his team tested involved the user pressing a key on the wheelchair’s human-machine interface, speaking a command, then confirming or rejecting the instruction via that same interface. Once the user confirmed the command, the mobility device guided the user along a path to the destination, while sensors attempted to detect obstacles in the way and adjust the mobility device accordingly to avoid them.
According to Pooja Viswanathan, CEO & founder of the Toronto-based Braze Mobility, research in the field of mobile assistive technology should also prioritize keeping these devices readily available to everyday consumers.
“Cost remains a major barrier,” she says. “Funding systems are often not designed to support advanced add-on intelligence unless there is very clear evidence of value and safety. Reliability is another barrier. A smart wheelchair has to work not just in ideal conditions, but in the messy, variable conditions of daily life. And there is also the human factors dimension. Users have different cognitive, motor, sensory, and environmental needs, so one solution rarely fits all.”
For its part, Braze makes blind-spot sensors for electric wheelchairs. The sensors detect obstacles in areas that can be difficult for a user to see. The sensors can also be added to any wheelchair to transform it into a smart wheelchair by providing multimodal alerts to the user. This approach attempts to support users rather than replace them.
According to Louise Devinge, a biomedical research engineer from IRISA (Research Institute of Computer Science and Random Systems) in Rennes, France, the increased complexity of smart wheelchairs demands more sensing. And that requires careful management of communication and synchronization within the wheelchair’s system. “The more sensing, computation, and autonomy you add,” she says, “the harder it becomes to ensure robust performance across the full range of real-world environments that wheelchair users encounter.”
In the near term, in other words, the field’s biggest challenge is not about replacing the wheelchair user with AI smarts but rather about designing better partnerships between the user and the technology.
This image shows data representations used by the 3D Driving Assistant. These include immutable sensor percepts such as laser scans and point clouds, as well as derived representations like the virtual laser scans and grid maps. Finally, the robot shape collection describes the wheelchair’s physical borders at different heights.DFKI
Mandel says he expects to see smart wheelchairs ready for the mainstream marketplace within 10 years.
Viswanathan says the REXASI-PRO system, while out of reach of present-day smart wheelchair technologies, is important for the longer term. “It reflects the more ambitious end of the smart wheelchair spectrum,” she says. “Its strengths appear to lie in intelligent navigation, advanced sensing, and the broader effort to build a wheelchair that can interpret and respond to complex environments in a more autonomous way. From a research standpoint, that is exactly the kind of work that pushes the field forward. It also appears to take seriously the importance of trustworthy and explainable AI, which is essential in any mobility technology where safety, reliability, and user confidence are paramount.”
Mandel says he’s ultimately in pursuit of the inspiration that got him into this field years ago. As a young researcher, he says, he helped develop a smart wheelchair system controllable with a head joystick.
However, Mandel says he realized after many trials that the smart wheelchair system he was working on had a long way to go because, as he says, “at that point in time, I realized that even persons that had severe handicaps [traveling through] a narrow passage, they did very, very well.
“And then I realized, okay, there is this need for this technology, but never underestimate what [wheelchair users] can do without it.”
The DFKI researchers presented their work earlier this month at the CSUN Assistive Technology Conference in Anaheim, Calif.
This article was supported by the IEEE Foundation and a Jon C. Taenzer fellowship grant.
2026-03-21 02:00:04
The rapid ascent of artificial intelligence and semiconductor manufacturing has created a paradox: Industries are booming yet they face a critical shortage of skilled workers. Demand for data center technicians, fabrication facility workers, and similar positions is growing. There aren’t enough candidates with the right skill sets to fill the in-demand jobs.
Although those technical roles are essential, they don’t always require a four-year degree—which has paved the way for skills-based microcredentials. By partnering with higher education institutions and training providers, industry leaders are helping to design targeted skills programs that quickly turn learners into job-ready technical professionals.
Because microcredentials are relatively new, consistency is key. Through its credentialing program, IEEE serves as a bridge between academia and industry. Developed and managed by IEEE Educational Activities, the program offers standardized credentials in collaboration with training organizations and universities seeking to provide skills-based qualifications outside formal degree programs. IEEE, as the world’s largest technical professional organization, has more than 30 years of experience offering industry-relevant credentials and expertise in global standardization.
IEEE is setting the benchmark for skills-based microcredentials by establishing a framework that includes assessment methods, qualifications for instructors and assessors, and criteria for skill levels.
A recent collaboration with the University of Southern California, in Los Angeles, for example, developed microcredentials for USC’s semiconductor cleanroom program. USC heads the CA Dreams microelectronics innovation hub.
“The IEEE framework allows us to rapidly prototype training programs and adapt on the fly in a way that building new university courses—much less degree programs—won’t allow.” —Adam Stieg
IEEE worked with USC to create standardized skills assessments and associated microcredentials so that industry hiring managers can recognize the newly developed skills. The microcredentials help people with or without four-year degrees join the semiconductor industry as cleanroom technicians or as engineers with cleanroom experience.
IEEE also has partnered with the California NanoSystems Institute at the University of California, Los Angeles, to create skills-based microcredentials for its cleanroom protocol and safety program.
Based on IEEE’s work designing microcredentials with USC, UCLA, and other leading academic institutions, three best practices have emerged.
Collaborate with industry prior to starting the design process. There isn’t a one-size-fits-all approach. Workforce needs vary based on industry sector, company size, and geography. Higher education institutions and training providers build relationships with companies and industry groups to create effective microcredential programs and methods of assessment.
Traditional academic cycles can be slow, but technology moves fast. A flexible skills-based microcredentials framework allows programs to create or pivot as new breakthroughs occur.
“Setting up a credit-bearing course is not easy. And in a rapidly changing environment, you need to pivot quickly,” says Adam Stieg, research scientist and associate director at UCLA’s CNSI. “IEEE skills-based microcredentials are a flexible way to keep up our curriculum aligned with an evolving technology landscape.”
Stieg’s team worked with IEEE to build a framework to create microcredentials for its cleanroom protocol and safety program, ensuring it kept pace with the industry’s evolution.
“The IEEE framework allows us to rapidly prototype training programs and adapt on the fly,” he says, “in a way that building new university courses—much less degree programs—won’t allow.”
Many of the technical roles companies are looking to fill in emerging fields such as AI, cybersecurity, and semiconductors are still being developed or are quickly evolving. The rapidly changing landscape requires continual communications and feedback among higher education, training providers, and industry.
“We struggle to have feedback loops through the education system to the industry and back again,” says Matt Francis, president and CEO of Ozark Integrated Circuits, in Fayetteville, Ark. Francis, who has served as IEEE Region 5 director, is an IEEE volunteer who supports workforce development for the semiconductor industry.
Creating consistent feedback loops is critical for generating consensus on the skills sets needed for microcredential programs, experts say, and it allows providers to update assessments as new tools and safety protocols enter the workplace.
“If we start thinking about having training frameworks used within companies that are essentially on some sort of standard and align with a microcredential, we can start to build consensus,” Francis says.
Through its credentialing program, IEEE is helping higher education and industry work together to bridge the technical workforce skills gap. Contact its team to learn how IEEE skills-based microcredentials can help you fill your workforce pipeline.
2026-03-19 22:45:05
A growing number of Nigerian companies are turning to kit-based assembly to bring electric vehicles to market in Africa. Lagos-based Saglev Micromobility Nigeria recently partnered with Dongfeng Motor Corporation, in Wuhan, China, to assemble 18-seat electric passenger vans from imported kits.
Kit-based assembly allows Nigerian firms to reduce costs, create jobs, and develop local technical expertise—key steps toward expanding EV access. Fully assembled and imported EVs face high tariffs that put them out of reach for many African consumers, whereas kit-based approaches make electric mobility more affordable today. Saglev’s initiative reflects a broader trend: CIG Motors, NEV Electric, and regional players in Cote D’Ivoire, Ghana, and Kenya are also leveraging imported kits to build local EV ecosystems, signaling that parts of West Africa are intent on catching up with global electrification efforts.
CIG Motors operates a kit-assembly plant in Lagos producing vehicles from Chinese automakers GAC Motor and Wuling Motors. These vehicles include the Wuling Bingo, a compact five-door electric hatchback, and the Hongguang Mini EV Macaron, a microcar with roughly 200 kilometers of range aimed at ride-share operators looking for ultralow-cost urban transport. NEV Electric focuses on electric buses and three-wheelers for urban transit and last-mile delivery.
Saglev’s CEO, Olu Faleye, emphasizes that Nigeria’s EV transition addresses both practical economic needs in addition to environmental goals. Beyond passenger transport, electric vehicles could help reduce one of Nigeria’s persistent agricultural challenges: post-harvest spoilage. Nigeria loses an estimated 30–40 million tonnes of food annually because of weak logistics and limited refrigeration infrastructure, according to the Organization for Technology Advancement of Cold Chain in West Africa.
Electric vans, mini-trucks, and three-wheel cargo vehicles could help close this gap because their batteries can power refrigeration systems during transport without relying on costly diesel fuel. As EV adoption grows and charging infrastructure expands, temperature-controlled transport could become more affordable, reducing spoilage, improving farmer incomes, and helping stabilize food supplies, the organization says.
“I don’t believe that the promised land is making a fully built EV on the ground here.”
–Olu Faleye, Saglev CEO
Beyond Nigeria, Mombasa, Kenya–based Associated Vehicle Assemblers has begun assembling electric taxis and minibuses from imported kits, and Ghana’s government is spurring kit-car assembly there under its national Automotive Development Plan. In Ghana, assemblers benefit from import-duty exemptions on kits and equipment, corporate tax breaks, and access to industrial infrastructure. Saglev is already availing itself of those benefits, at its kit-assembly plant in Accra. The company says it also plans to expand its assembly operations to Cote D’Ivoire.
Despite these signs that West Africa’s EV ecosystem is gaining traction, limited grid reliability and sparse public charging infrastructure remain major barriers to widespread EV adoption. Urban households in Nigeria experience roughly six or seven blackouts per week, each lasting about 12 hours, according to Nigeria’s National Bureau of Statistics. That’s more downtime each day than the average U.S. household experiences in a year. More than 40 percent of households rely on generators, which supply about 44 percent of residential electricity, according to research by Stears and Sterling Bank.
Many early EV adopters therefore charge vehicles using gasoline or diesel generators. Faleye notes that Nigerians have long relied on such workarounds and expects fossil fuels to remain part of the EV charging equation for the foreseeable future—at least until falling costs for solar panels and battery storage make cleaner charging viable.
He acknowledges that charging EVs using hydrocarbons is fraught from an environmental perspective, but he points out that the practice at least brings other benefits of EVs, including lower maintenance costs and the EVs’ synergies with refrigeration and transportation logistics. And he points to a 2020 peer-reviewed study in the journal Environmental and Climate Technologies that compared the overall efficiency of internal combustion vehicles and electric vehicles across the full well-to-wheel energy chain. The study’s conclusion: Even after accounting for conversion losses, generating electricity with a diesel or gasoline generator to power an electric vehicle can remain just as efficient overall as burning the same fuel directly in a vehicle’s internal combustion engine.
The approach taken by Saglev and other Nigerian kit-car builders shows how local assembly can advance EV adoption even where infrastructure remains unreliable. By starting with kits, companies can deploy practical electric mobility solutions now while building the supply chains and technical expertise needed for more resource-intensive localized production.
Still, when asked whether Saglev plans to eventually move beyond kit assembly to independent design and manufacturing of EVs, Faleye calls such a move impractical.
“I don’t believe that the promised land is making a fully built EV on the ground here,” he says. “For me to do efficient vehicle manufacturing, I’d need a lot of robotics and 3D printing. That expense is unnecessary—it would just increase costs and make EVs more expensive.”
In a country where electricity can disappear for days, Nigeria’s kit-based EV strategy highlights a practical truth: incremental progress and ingenuity may matter more than perfect infrastructure. For Saglev, every kit-based vehicle rolling off the line is not just a van or bus—it’s a step toward an EV ecosystem that works for Nigeria’s realities today.
2026-03-19 20:00:05
One morning in May 2019, a cardiac surgeon stepped into the operating room at Boston Children’s Hospital more prepared than ever before to perform a high-risk procedure to rebuild a child’s heart. The surgeon was experienced, but he had an additional advantage: He had already performed the procedure on this child dozens of times—virtually. He knew exactly what to do before the first cut was made. Even more important, he knew which strategies would provide the best possible outcome for the child whose life was in his hands.
How was this possible? Over the prior weeks, the hospital’s surgical and cardio-engineering teams had come together to build a fully functioning model of the child’s heart and surrounding vascular system from MRI and CT scans. They began by carefully converting the medical imaging into a 3D model, then used physics to bring the 3D heart to life, creating a dynamic digital replica of the patient’s physiology. The mock-up reproduced this particular heart’s unique behavior, including details of blood flow, pressure differentials, and muscle-tissue stresses.
This type of model, known as a virtual twin, can do more than identify medical problems—it can provide detailed diagnostic insights. In Boston, the team used the model to predict how the child’s heart would respond to any cut or stitch, allowing the surgeon to test many strategies to find the best one for this patient’s exact anatomy.
That day, the stakes were high. With the patient’s unique condition—a heart defect in which large holes between the atria and ventricles were causing blood to flow between all four chambers—there was no manual or textbook to fully guide the doctors. The condition strains the lungs, so the doctors planned an open-heart surgery to reroute deoxygenated blood from the lower body directly to the lungs, bypassing the heart. Typically with this kind of surgery, decisions would be made on the fly, under demanding conditions, and with high uncertainty. But in this case, the plan had been tested in advance, and the entire team had rehearsed it before the first incision. The surgery was a complete success.
Such procedures have become routine at the Boston hospital. Since that first patient, nearly 2,000 procedures have been guided by virtual-twin modeling. This is the power of the technology behind the Living Heart Project, which I launched in 2014, five years before that first procedure. The project started as an exploratory initiative to see if modeling the human heart was possible. Now with more than 150 member organizations across 28 countries, the project includes dozens of multidisciplinary teams that regularly use multiscale virtual twins of the heart and other vital organs.
This technology is reshaping how we understand and treat the human body. To reach this transformative moment, we had to solve a fundamental challenge: building a digital heart accurate enough—and trustworthy enough—to guide real clinical decisions.
Now entering its second decade, the Living Heart Project was born in part from a personal conviction. For many years, I had watched helplessly as my daughter Jesse faced endless diagnostic uncertainty due to a rare congenital heart condition in which the position of the ventricles is reversed, threatening her life as she grew. As an engineer, I understood that the heart was an array of pumping chambers, controlled by an electrical signal and its blood flow carefully regulated by valves. Yet I struggled to grasp the unique structure and behavior of my daughter’s heart well enough to contribute meaningfully to her care. Her specialists knew the bleak forecast children like her faced if left untreated, but because every heart with her condition is anatomically unique, they had little more than their best guesses to guide their decisions about what to do and when to do it. With each specialist, a new guess.
Then my engineering curiosity sparked a question that has guided my career ever since: Why can’t we simulate the human body the way we simulate a car or a plane?
At a visualization center in Boston, VR imagery helps the mother of a young girl with a complex heart defect understand the inner workings of her child’s heart. Dassault Systèmes
I had spent my career developing powerful computational tools to help engineers build digital models of complex mechanical systems, using models that ranged from the interactions of individual atoms to the components of entire vehicles. What most of these models had in common was the use of physics to predict behavior and optimize performance. But in medicine today, those same physics-based approaches rarely inform decision-making. In most clinical settings, treatment decisions still hinge on judgments drawn from static 2D images, statistical guidelines, and retrospective studies.
This was not always the case. Historically, physics was central to medicine. The word “physician” itself traces back to the Latin physica, which translates to “natural science.” Early doctors were, in a sense, applied physicists. They understood the heart as a pump, the lungs as bellows, and the body as a dynamic system. To be a physician meant you were a master of physics as it applied to the human body.
As medicine matured, biology and chemistry grew to dominate the field, and the knowledge of physics got left behind. But for patients like my daughter, that child in Boston, and millions like them, outcomes are governed by mechanics. No pill or ointment—no chemistry-based solution—would help, only physics. While I did not realize it at the time, virtual twins can reunite modern physicians with their roots, using engineering principles, simulation science, and artificial intelligence.
The LHP concept was simple: Could we combine what hundreds of experts across many specialties knew about the human heart to build a digital twin accurate enough to be trusted, flexible enough to personalize, and predictive enough to guide clinical care?
We invited researchers, clinicians, device and drug companies, and government regulators to share their data, tools, and knowledge toward a common goal that would lift the entire field of medicine. The Living Heart Project launched with a dozen or so institutions on board. Within a year, we had created the first fully functional virtual twin of the human heart.
The Living Heart was not an anatomical rendering, tuned to simply replicate what we observed. It was a first-principles model, coupling the network of fibers in the heart’s electrical system, the biological battery that keeps us alive, with the heart’s mechanical response, the muscle contractions that we know as the heartbeat.
The Living Heart virtual twin simulates how the heart beats, offering different views to help scientists and doctors better predict how it will respond to disease or treatment. The center view shows the fine engineering mesh, the detailed framework that allows computers to model the heart’s motion. The image on the right uses colors to show the electrical wave that drives the heartbeat as it conducts through the muscle, and the image on the left shows how much strain is on the tissue as it stretches and squeezes. Dassault Systèmes
Academic researchers had long explored computational models of the heart, but those projects were typically limited by the technology they had access to. Our version was built on industrial-grade simulation software from Dassault Systèmes, a company best known for modeling tools used in aerospace and automotive engineering, where I was working to develop the engineering simulation division. This platform gave teams the tools to personalize an individual heart model using the patient’s MRI and CT data, blood-pressure readings, and echocardiogram measurements, directly linking scans to simulations.
Surgeons then began using the Living Heart to model procedures. Device makers used it to design and test implants. Pharmaceutical companies used it to evaluate drug effects such as toxicity. Hundreds of publications have emerged from the project, and because they all share the same foundation, the findings can be reproduced, reused, and built upon. With each application, the research community’s understanding of the heart snowballed.
Early on, we also addressed an essential requirement for these innovations to make it to patients: regulatory acceptance. Within the project’s first year, the U.S Food and Drug Administration agreed to join the project as an observer. Over the next several years, methods for using virtual-heart models as scientific evidence began to take shape within regulatory research programs. In 2019, we formalized a second five-year collaboration with the FDA’s Center for Devices and Radiological Health with a specific goal.
That goal was to use the heart model to create a virtual patient population and re-create a pivotal trial of a previously approved device for repairing the heart’s mitral valve. This helped our team learn how to create such a population, and let the FDA experiment with evaluating virtual evidence as a replacement for evidence from flesh-and-blood patients. In August 2024, we published the results, creating the first FDA-led guidelines for in silico clinical trials and establishing a new paradigm for streamlining and reducing risk in the entire clinical-trial process.
In 10 years, we went from a concept that many people doubted could be achieved to regulatory reality. But building the heart was only the beginning. Following the template set by the heart team, we’ve expanded the project to develop virtual twins of other organs, including the lungs, liver, brain, eyes, and gut. Each corresponds to a different medical domain, which has its own community, data types, and clinical use cases. Working independently, these teams are progressing toward a breakthrough in our understanding of the human body: a multiscale, modular twin platform where each organ twin could plug into a unified virtual human.
A cardiac digital twin starts with medical imaging, typically MRI, CT, or both. The slices are reconstructed into the 3D geometry of the heart and connected vessels. The geometry of the whole organ must then be segmented into its constituent parts, so each substructure—atria, ventricles, valves, and so on—can be assigned their unique properties.
At this point, the object is converted to a functional, computational model that can represent how the various cardiac tissues deform under load—the mechanics. The complete digital twin model becomes “living” when we integrate the electrical fiber network that drives mechanical contractions in the muscle tissue.
Each part of the heart, such as the left ventricle [left], is superimposed with a detailed digital mesh to re-create its physiology. These pieces come together to form an anatomically accurate rendering of the whole organ [right].Dassault Systèmes
To simulate circulation, the twin adds computational models of hemodynamics, the physics of blood flow and pressure. The model is constrained by boundary conditions of blood flow, valve behavior, and vascular resistance set to closely match human physiology. This lets the model predict blood flow patterns, pressure differentials, and tissue stresses.
Finally, the model is personalized and calibrated using available patient data, such as how much the volume of the heart chambers changes during the cardiac cycle, pressure measurements, and the timing of electrical pulses. This means the twin reflects not only the patient’s anatomy but how their specific heart functions.
When the FDA in silico clinical trial initiative launched in 2019, the project’s focus shifted from these handcrafted virtual twins of specific patients to cohorts large enough to stand in for entire trial populations. That scale is feasible today only because virtual twins have converged with generative AI. Modeling thousands of patients’ responses to a treatment or projecting years of disease progression is prohibitively slow with conventional digital-twin simulations. Generative AI removes that bottleneck.
AI boosts the capability of virtual twins in two complementary ways. First, machine learning algorithms are unrivaled at integrating the patchwork of imaging, sensor, and clinical records needed to build a high-fidelity twin. The algorithms rapidly search thousands of model permutations, benchmark each against patient data, and converge on the most accurate representation. Workflows that once required months of manual tuning can now be completed in days, making it realistic to spin up population-scale cohorts or to personalize a single twin on the fly in the clinic.
Second, enriching AI models’ training sets with data from validated virtual patients grounds the AI simulations in physics. By contrast, many conventional AI predictions for patient trajectories rely on statistical modeling trained on retrospective datasets. Such models can drift beyond physiological reality, but virtual twins anchor predictions in the laws of hemodynamics, electrophysiology, and tissue mechanics. This added rigor is indispensable for both research and clinical care—especially in areas where real-world data are scarce, whether because a disease is rare or because certain patient populations, such as children, are underrepresented in existing datasets.
On the research side, the FDA-sponsored In Silico Clinical Trial Project that we completed in 2024 opened a new world for medical innovations. A conventional clinical trial may take a decade, and 90 percent of new drug treatments fail in the process. Virtual twins, combined with AI methods, allow researchers to design and test treatments quickly in a simulated human environment. With a small library of virtual twins, AI models can rapidly create expansive virtual patient cohorts to cover any subset of the general population. As clinical data becomes available, it can be added into the training set to increase reliability and enable better predictions.
The Living Heart Project has expanded beyond the heart, modeling organs throughout the body. The 3D brain reconstruction [top] shows major pathways in the brain’s white matter connecting color-coded regions of the brain. The lung virtual twin [middle] combines the organ’s geometry with a physics-based simulation of air flowing down the trachea and into the bronchi. And the cross section of a patient’s foot [bottom] shows points of strain in the soft tissue when bearing weight. Dassault Systèmes
Virtual twin cohorts can represent a realistic population by building individual “virtual patients” that vary by age, gender, race, weight, disease state, comorbidities, and lifestyle factors. These twins can be used as a rich training set for the AI model, which can expand the cohort from dozens to hundreds of thousands. Next the virtual cohort can be filtered to identify patients likely to respond to a treatment, increasing the chances of a successful trial for the target population.
The trial design can also include a sampling of patient types less likely to respond or with elevated risk factors, thus allowing regulators and clinicians to understand the risks to the broader population without jeopardizing overall trial success. This methodology enhances precision and efficiency in clinical research, providing population-level insights previously available only after many years of real-world evidence.
Of course, though today’s heart digital twins are powerful, they’re not perfect replicas. Their accuracy is bounded by three main factors: what we can measure (for example, image resolution or the uncertainty of how tissue behaves in real life), what we must assume about the physiology, and what we can validate against real outcomes. Many inputs, like scarring, microvascular function, or drug effects are difficult to capture clinically, so models often rely on population data or indirect estimation. That means predictions can be highly reliable for certain questions but remain less certain for others. Additionally, today’s digital twins lack validation for predicting long-term outcomes years in the future, because the technology has been in use for only a few years.
Over time, each of these limitations will steadily shrink. Richer, more standardized data will tighten personalization of the models. AI tools will help automate labor-intensive steps. And the collection of longitudinal data will improve the model’s ability to reliably predict how the body will evolve over time.
Throughout modern medicine, new technologies have sharpened our ability to diagnose, providing ever-clearer images, lab data, and analytics that tell physicians what is presently happening inside a patient’s body. Virtual twins shift that paradigm, giving clinicians a predictive tool.
This “Living Lung” virtual-twin simulation shows strain patterns during breathing. Mona Eskandari/UC Riverside
Early demonstrations are already appearing in many areas of medicine, including cardiology, orthopedics, and oncology. Soon, doctors will also be able to collaborate across specialties, using a patient-specific virtual twin as the common ground for discussing potential interactions or side effects they couldn’t predict independently.
Although these applications will take some time to become the standard in clinical care, more changes are on the horizon. Real-time data from wearables, for example, could continuously update a patient’s personalized virtual twin. This approach could empower patients to understand and engage more deeply in their care, as they could see the direct effects of medical and lifestyle changes. In parallel, their doctors could get comprehensive data feeds, using virtual twins to monitor progress.
Imagine a digital companion that shows how your particular heart will react to different amounts of salt intake, stress, or sleep deprivation. Or a visual explanation of how your upcoming surgery will affect your circulation or breathing. Virtual twins could demystify the body for patients, fostering trust and encouraging proactive health decisions.
With the Living Heart Project, we’re bringing physics back to physicians. Modern physicians won’t need to be physicists, any more than they need to be chemists to use pharmacology. However, to benefit from the new technology, they will need to adapt their approach to care.
This means no longer seeing the body as a collection of discrete organs and considering only symptoms, but instead viewing it as a dynamic system that can be understood, and in most cases, guided toward health. It means no longer guessing what might work but knowing—because the simulation has already shown the result. By better integrating engineering principles into medicine, we can redefine it as a field of precision, rooted in the unchanging laws of nature. The modern physician will be a true physicist of the body and an engineer of health.