The Department of Veterans Affairs is participating in a series of MIT “GrandHacks,” problem-solving sessions aimed at tackling some of the VA’s biggest health care challenges, reports Patricia Kime for Military.com. The sessions, explains Kime, “bring together teams of students, entrepreneurs, tech gurus, health providers, patients, insurers and academicians to find solutions to problems in a short amount of time.”
A sensor developed by MIT researchers could make diagnosing sepsis easier, quicker and more affordable, reports Darrell Etherington for TechCrunch. Etherington explains that the sensor, which “employs microfluidics to detect the presence of key proteins in the blood,” could have “a huge potential impact, as sepsis is one of the leading causes of death in hospitals.”
Writing for STAT, Prof. Jonathan Gruber examines his research showing that while doctors have more information about different tests and treatments, they make decisions similar to their patients when receiving care. Gruber says this finding suggests that to improve health care decision-making, financial incentives and other approaches are needed that go beyond providing patients with more information.
CNN reporter Nell Lewis spotlights how MIT researchers have developed an algorithm that can help predict from a mammogram a patient’s risk of developing breast cancer. “In the early stages cancer is a treatable disease,” says Barzilay. “If we can identify many more women early enough, and either prevent their disease or treat them at the earliest stages, this will make a huge difference.”
A study co-authored by Prof. Cynthia Breazeal found that a “social robot” teddy bear “boosted spirits, eased anxiety and even lowered perceived pain levels” among Boston Children’s Hospital patients aged 3 to 10 years old, reports Robert Preidt for US News & World Report. “We want technology to support everyone who's invested in the quality care of a child," says Breazeal.
A new AI prediction model developed at MIT could detect breast cancer up to five years in advance. The researchers hope this technique “can also be used to improve detection of other diseases that have similar problems with existing risk models, with far too many gaps and lower degrees of accuracy,” writes Darrell Etherington for TechCrunch.
Writing for the Financial Times about how technology is advancing the field of health care, John Browne spotlights Prof. Bob Langer’s work developing new methods of delivering drugs with improved precision. Browne explains that Langer is working on “a device smaller than a grain of rice that he can inject into a tumour to test the efficacy of dozens of chemotherapy agents in parallel.”
Writing for Times Higher Education, senior lecturer Anjali Sastry argues that entrepreneurship is a key component in finding solutions to complex global health problems. Sastry spotlights how MIT students are provided with hands-on opportunities to “learn analytics, systems thinking, effective business models and entrepreneurial processes. They aren’t just learning how to maximize profits, but ways to understand the market and craft systems.”
Fast Company reporter Michael Grothaus writes that CSAIL researchers have developed a deep learning model that could predict whether a woman might develop breast cancer. The system “could accurately predict about 31% of all cancer patients in a high-risk category,” Grothaus explains, which is “significantly better than traditional ways of predicting breast cancer risks.”
WCVB-TV’s Jennifer Eagan reports that researchers from MIT and MGH have developed a deep learning model that can predict a patient’s risk of developing breast cancer in the future from a mammogram image. Prof. Regina Barzilay explains that the model “can look at lots of pixels and variations of the pixels and capture very subtle patterns.”
HealthDay News reporter Amy Norton writes that MIT researchers have developed an AI system that can help predict a woman’s risk of developing breast cancer and provide more personalized care. “If you know a woman is at high risk, maybe she can be screened more frequently, or be screened using MRI,” explains graduate student Adam Yala.
In an article for The Wall Street Journal, senior lecturer Robert Pozen argues that a new Medicare for All proposal “differs from Medicare in fundamental ways—with much broader coverage, no cost sharing, and fewer choices of health-care plans.”
Prof. Regina Barzilay speaks with NPR reporter Richard Harris about her work developing AI systems aimed at improving identification of breast cancer in mammograms, inspired by her experience with the disease. “At every point of my treatment, there would be some point of uncertainty, and I would say, 'Gosh, I wish we had the technology to solve it,’” says Barzilay.
Axios reporter Kaveh Waddell writes about a new study by MIT researchers that examines the potential impact of adversarial attacks on health care systems. “If someone sending in data for analysis has a different goal than the owner of the system doing the analysis, there's a potential for funny business,” Waddell explains.
MIT researchers have found that some inactive ingredients in medications could play a role in triggering irritation or allergic reactions, reports Bob McDonald for CBC Radio. The researchers hope that, “pharmaceutical companies provide more information to doctors, and that alternative drug formulas can be developed for people with allergies or sensitivities.”