MIT researchers have developed microneedle patches that are capable of restoring hair growth in alopecia areata patients, reports Ernie Mundell for HealthDay. The team’s approach includes a, “patch containing myriad microneedles that is applied to the scalp,” writes Mundell. “It releases drugs to reset the immune system so it stops attacking follicles.”
Prof. Giovanni Traverso and colleagues have developed a new ingestible sensor that could be used to help diagnose gastrointestinal conditions, reports Jeremy Hsu for New Scientist. “Eventually, the futuristic device could provide treatments for gut illnesses through electrical stimulation via additional electrodes embedded in the sensor,” Hsu notes.
Prof. Linda Griffith and Stuart Orkin '67 were named to this year’s Time 100 Health list, which recognizes innovators leading the way to new health solutions. Griffith, who was honored for her work engineering a uterine organoid to study endometriosis, explains that in the future engineered organoids could be used to find the most effective treatments for patients. “We have all the genetic information and all the information from the patient’s exposure to infections, environmental chemicals, and stress that would cause the tissues to become deranged in some way, all captured in that organoid,” Griffith explains.
MIT researchers have found that “when an AI tool for radiologists produced a wrong answer, doctors were more likely to come to the wrong conclusion in their diagnoses,” report Daniel Payne, Carmen Paun, Ruth Reader and Erin Schumaker for Politico. “The study explored the findings of 140 radiologists using AI to make diagnoses based on chest X-rays,” they write. “How AI affected care wasn’t dependent on the doctors’ levels of experience, specialty or performance. And lower-performing radiologists didn’t benefit more from AI assistance than their peers.”
Writing for Fast Company, Senior Lecturer Guadalupe Hayes-Mota '08, SM '16, MBA '16 shares methods to address the influence of AI in healthcare. “Despite these advances [of AI in healthcare], the full spectrum of AI’s potential remains largely untapped,” explains Hayes-Mota. “Systemic hurdles such as data privacy concerns, the absence of standardized data protocols, regulatory complexities, and ethical dilemmas are compounded by an inherent resistance to change within the healthcare profession. These barriers underscore the urgent need for transformative action from all stakeholders to fully harness AI’s capabilities.”
Senior Lecturer Guadalupe Hayes-Mota writes for Forbes about the ways AI is reshaping drug development. “In the next three years, we can anticipate a more streamlined, efficient and cost-effective drug development process, ultimately leading to faster access to life-saving drugs for patients worldwide,” Hayes-Mota writes. “This is not just an evolution; it is a revolution in healthcare powered by the intelligence of machines.”
Elemind Technologies, a neuro-tech startup founded by scientists from MIT and elsewhere, is developing, “an approach that redirects brain wave through non-invasive stimulation – using sound, light, touch and electric pulses – to potentially address a range of neurological conditions in a more targeted ways than drugs,” reports Robert Weisman for The Boston Globe.
Researchers from MIT and elsewhere have developed an AI model that is capable of identifying 3 ½ times more people who are at high-risk for developing pancreatic cancer than current standards, reports Felice J. Freyer for The Boston Globe. “This work has the potential to enlarge the group of pancreatic cancer patients who can benefit from screening from 10 percent to 35 percent,” explains Freyer. “The group hopes its model will eventually help detect risk of other hard-to-find cancers, like ovarian.”
Prof. Ron Weiss co-founded Strand Therapeutics, a biotech company developing mRNA therapies, reports Emily Mullin for Wired. “The notion is that genetic circuits can really have significant impact on safety and efficacy,” says Weiss. “This begins to really open up the door for creating therapies whose sophistication can match the underlying complexity of biology.”
Prof. Jonathan Weissman and his colleagues have developed a new tool for monitoring changes in human blood cells, which could one day help researchers predict disease risk, reports Megan Molteni for STAT. “The technology paves the way for a day in the not too distant future where it is conceivable that from a simple blood draw, a doctor could get a sense of what’s going on in that patient’s bone marrow,” writes Molteni, “picking up perturbations there that could help predict a diverse range of diseases.”
Carmen Martin-Alonso PhD '23 speaks with Zakiya Whatley on the Science podcast to discuss her recent research focused on developing new methods to improve liquid biopsies for cancer. “I think this is super, super promising for the field of oncology where having more sensitive ctDNA-based liquid biopsies could really transform patient management,” says Alonso. “And in the same way as radio label converse agents have transformed imaging, we think that priming agents could transform the utility of liquid biopsies.”
Alicia Chong Rodriguez SM ’17, SM ’18 founded Bloomer Tech, a health tech startup that aims to improve health care diagnostics for women using medical-grade data to develop new therapies and care models, reports Carol Eisenberg for The Washington Post. Rodriguez and her colleagues "developed, patented and tested flexible washable circuits to turn articles of clothing into devices that can relay reams of information to the wearer’s smartphone,” writes Eisenberg.
Popular Mechanics reporter Jill Waldbieser spotlights Prof. Hugh Herr and his work developing prosthetic limbs that integrate with their human hosts using a surgical technique that preserves the sensation in artificial limbs. “In the future, on the order of five years or so, we’ll be so good at this, we’ll completely restore the signals from the prosthetic to the brain and from the brain to the prosthetic, like the limb was never amputated,” says Herr.
Researchers at MIT and elsewhere developed an artificial intelligence predictive model that can be used to detect which strains of Covid-19 could become dominant and lead to a new wave of illness, reports Ruth Reader, Carmen Paun, Daniel Payne and Eric Schumaker for Politico. The model, “found three strong predictors of a dominant variant: the number of infections a strain causes in its first week relative to the number of times it appears in sequencing, the number of mutations in the spike protein, and the number of weeks since the current dominant variant began circulating,” they note.