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Tag: Marzyeh Ghassemi

Portrait of Hammaad Adam

Growing our donated organ supply

For those in need of one, an organ transplant is a matter of life and death.

Every year, the medical procedure gives thousands of people with advanced or end-stage diseases extended life. This “second chance” is heavily dependent on the availability, compatibility, and proximity of a precious resource that can’t be simply bought, grown, or manufactured — at least not yet.

Instead, organs must be given — cut from one body and implanted into another. And because living organ donation is only viable in certain cases, many organs are only available for donation after the donor’s death.

Unsurprisingly, the logistical and ethical complexity of distributing a limited number of transplant organs to a growing wait list of patients has received much attention. There’s an important part of the process that has received less focus, however, and which may hold significant untapped potential: organ procurement itself. Learn more
Screenshot of a video with a title that reads "AI and Responsible Clinical Implementation"

AI Developers Should Understand the Risks of Deploying Their Clinical Tools, MIT Expert Says

AI applications for health care should be designed to function well in different settings and across different populations, says Marzyeh Ghassemi, PhD (Video), whose work at the Massachusetts Institute of Technology (MIT) focuses on creating “healthy” machine learning (ML) models that are “robust, private, and fair.” The way AI-generated clinical advice is presented to physicians is also important for reducing harms, according to Ghassemi, who is an assistant professor at MIT’s Department of Electrical Engineering and Computer Science and Institute for Medical Engineering and Science. And, she says, developers should be aware that they have a responsibility to clinicians and patients who could one day be affected by their tools. Learn more
Plane taking off from a handheld tablet surrounded by white geometric lines and floating blue pixels.

Stratospheric safety standards: How aviation could steer regulation of AI in health

What is the likelihood of dying in a plane crash? According to a 2022 report released by the International Air Transport Association, the industry fatality risk is 0.11. In other words, on average, a person would need to take a flight every day for 25,214 years to have a 100 percent chance of experiencing a fatal accident. Long touted as one of the safest modes of transportation, the highly regulated aviation industry has MIT scientists thinking that it may hold the key to regulating artificial intelligence in health care.

Marzyeh Ghassemi, an assistant professor at the MIT Department of Electrical Engineering and Computer Science (EECS) and Institute of Medical Engineering Sciences, and Julie Shah, an H.N. Slater Professor of Aeronautics and Astronautics at MIT, share an interest in the challenges of transparency in AI models. After chatting in early 2023, they realized that aviation could serve as a model to ensure that marginalized patients are not harmed by biased AI models. Learn more
Marzyeh Ghassemi seated on a bench.

ChatGPT one year on: who is using it, how and why?

On 30 November 2022, the technology company OpenAI released ChatGPT — a chatbot built to respond to prompts in a human-like manner. It has taken the scientific community and the public by storm, attracting one million users in the first 5 days alone; that number now totals more than 180 million. Seven researchers told Nature how it has changed their approach. Learn more
Illustration of a computer in a stone age setting

How an archeological approach can help leverage biased data in AI to improve medicine

Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values. Learn more
Group of high school students posing for a group photo with MIT President Emerita Susan Hockfield

How to help high schoolers prepare for the rise of artificial intelligence

A one-week summer program aims to foster a deeper understanding of machine-learning approaches in health among curious young minds. Learn more
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