Her surgical techniques provide a sense of touch to people with prosthetic limbs.
As a child, Shriya Srinivasan, now a postdoctoral researcher in biomedical engineering at MIT, witnessed the challenges of living with prosthetic limbs. A friend had been born with missing limbs and used prosthetics; like amputees, whose nerves have been severed, her brain lacked the important neural signals that enable most people to feel objects, maintain balance, and sense their body’s position in space. Srinivasan has invented two new types of surgical techniques that could soon help people using prosthetic limbs regain their sense of touch.
Her first innovation, which she developed as an MIT doctoral student, involves grafting small segments of muscle onto the residual limb; the goal is to enhance the mind’s awareness of limb position and movement. Patients who underwent a version of this procedure in clinical trials have exhibited far greater control over their prostheses—and less pain—than those with traditional amputations. Her second procedure has shown early promise in re-creating touch. It works by fitting a person’s residual limb with flaps of skin from the fingertips or feet, encased by a muscle graft and electrode. A prosthetic arm or hand is then equipped with sensors and a wireless transmitter; when it touches an object, it conveys that sensation to the natural sensors on the grafted skin—which relay it on to the brain. Both techniques can be performed either as part of an amputation procedure or in patients with previous amputations.
Ultimately, Srinivasan hopes her work will help make using a prosthetic limb far more like the real thing—while initiating a broader shift in our approach to amputation from a form of salvage to a method of restoring mobility.
His bionic hands combine sensitivity with affordability.
Aadeel Akhtar has developed algorithms that make upper-limb prosthetics much more functional to use. Some send electrical currents to stimulate the nerves so that users can “feel” what their prosthetics are touching; others record the electrical currents caused by muscle contractions, making it possible to control movement. Akhtar has been doing this work for over 10 years, first as a doctoral researcher at the University of Illinois at Urbana-Champaign and then, starting in 2015, as the founder of the robotic-limb startup Psyonic.
Akhtar holds four patents on advances in prosthetics that have all gone into Psyonic’s first product, the Ability Hand. The Ability Hand was designed to be controlled by both muscle sensors and Bluetooth (yes, there’s an app!) and provide tactile sensory data to its user, all while withstanding the normal stresses of everyday life (like getting knocked against a table) without cracking.
Akhtar’s team of 20 designed with affordability in mind, he says, and built a hand inexpensive enough to be covered by Medicare. This means far more people in the US will be able to afford it. Previously, Akhtar explains, the only insurance that covered bionic hands was associated with veterans’ benefits and worker’s compensation claims, which he estimates cover only about 10% of the need in the United States. Participation from Medicare would make bionic hands available to 75% of individuals in the US who need them. “If Medicare covers it, then other insurers usually follow suit,” Akhtar says.
His AI systems identify better treatments for tuberculosis.
Before covid-19, tuberculosis was the most dangerous infection in the world, killing more than 1.5 million people annually. The problem prompted Sriram Chandrasekaran to build AI tools to identify potent drug combinations to treat it. His goal is to boost the effectiveness of existing antibiotics to combat drug resistance among TB patients.
Drug-resistant infections occur when people don’t finish their course of treatment or are treated incorrectly. They can also occur when people come in contact with a patient infected with drug-resistant bacteria. While a typical TB treatment regimen lasts six to nine months, a drug-resistant case takes 18 to 24 months to treat. Chandrasekaran wants to drastically reduce this timeline. Curing patients faster could also save thousands of dollars in treatment costs.
Chandrasekaran’s systems predict the effectiveness of various drug combinations for TB. “We’ve found some really surprising ones,” he says, including an antipsychotic drug that would enhance the potency of existing antibiotics. He and his team confirmed the results against the TB bacterium in the lab.
Many drugs work in the lab but aren’t effective in the body, and Chandrasekaran wanted to make sure his algorithms take this into account. One system he built simulates characteristics of the infection site—for example, how much oxygen it gets or whether amino acids are present, which can affect a drug’s effectiveness. Chandrasekaran’s lab is now identifying promising drug combinations for use in clinical trials of treatment against drug-resistant TB.
She employs AI to get to the roots of health disparities across race, gender, and class.
Cornell University computer scientist Emma Pierson uses AI and emerging data science models to reveal how health disparities arise between sexes, races, socioeconomic groups, and other demographic categories. “These are fancy ways of saying I use math to find patterns in large data sets, and the specific types of patterns I’m looking for are attempting to answer sort of old questions in health and social sciences,” she says.
The “old questions” she’s investigating range widely in their specifics, but she focuses on uncovering how systemic inequalities in public health come to be, and pointing at ways to dismantle them. For example, by analyzing mobile-phone data, she recently showed that particular “superspreader” locations were primarily responsible for transmitting covid-19 across populations, and that low-income and minority communities suffered greater risk of exposure.
Beyond the pandemic, Pierson’s research team recently examined nearly a decade’s worth of data to show the extent of racial disparities in traffic stops made by police across the US. She analyzed menstrual health data from millions of women in 109 countries to demonstrate how effects on mood and behavior are experienced universally, seeking to destigmatize discussions around women’s health. And she used deep learning to study data on knee pain, revealing that the problem was often poorly measured and even exacerbated in patients from racially underserved groups and lower economic backgrounds.
Pierson has made it her mission to see this work break out of the confines of academia. She’s a fairly regular contributor to the New York Times and the Atlantic, offering up a layperson’s account of her work to a large audience. And she engages directly with organizations that can pressure policymakers. Her work on racial disparities in traffic stops ultimately led the Los Angeles Police Department to announce that it would reduce the number of random stops it conducted, and state departments of health leaned on her covid-19 findings to determine how to safely reopen businesses.
A self-professed math nerd, Pierson simultaneously earned a bachelor’s degree in physics and a master’s in computer science at Stanford before moving to Oxford as a Rhodes scholar, where she earned a master’s in statistics, afterward earning a doctorate in computer science at Stanford.
“I wanted to work on problems that were very concretely tied to people’s lives,” she says. “I think this sense was particularly driven by my own family’s medical history.” In December 2011, Pierson learned she was carrying a genetic mutation that increases her risk of breast and ovarian cancer, and it drove her to focus on work that could make an impact in health care and medicine.
Industries like health care deal with insanely large data sets that can really be understood only with the kinds of analytical techniques Pierson has mastered. The data might be the genomes of thousands of people, containing millions upon millions of data points, or medical images from many different patients, representing terabytes of information. AI tools can sort through this data and look for patterns that no human could readily identify. “Computational methods are not optional here,” Pierson says. “They’re the only solution.”