Tag: AI in health care
-
MIT-Takeda Program wraps up with 16 publications, a patent, and nearly two dozen projects completed
When the Takeda Pharmaceutical Co. and the MIT School of Engineering launched their collaboration focused on artificial intelligence in health care and drug development in February 2020, society was on the cusp of a globe-altering pandemic and AI was far from the buzzword it is today. As the programย concludes, the world looks very different. AIย has…
-
What to do about AI in health?
Before a drug is approved by the U.S. Food and Drug Administration (FDA), it must demonstrate both safety and efficacy. However, the FDA does not require an understanding a drugโs mechanism of action for approval. This acceptance of results without explanation raises the question of whether the “black box” decision-making process of a safe and…
-
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…
-
2023-24 Takeda Fellows: Advancing research at the intersection of AI and health
The School of Engineering has selected 13 new Takeda Fellows for the 2023-24 academic year. With support from Takeda, the graduate students will conduct pathbreaking research ranging from remote health monitoring for virtual clinical trials to ingestible devices for at-home, long-term diagnostics. Now in its fourth year, the MIT-Takeda Program, a collaboration between MITโs School…
-
How to help high schoolers prepare for the rise of artificial intelligence
Should artificial intelligence be allowed to make care decisions for patients? Though the future of AI may conjure up doomsday visions of robots and computers intent on rendering human existence superfluous, the MIT Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) addressed questions surrounding the use of AI in health through their…
-
MIT researchers combine deep learning and physics to fix motion-corrupted MRI scans
Compared to other imaging modalities like X-rays or CT scans, MRI scans provide high-quality soft tissue contrast. Unfortunately, MRI is highly sensitive to motion, with even the smallest of movements resulting in image artifacts. These artifacts put patients at risk of misdiagnoses or inappropriate treatment when critical details are obscured from the physician. But researchers…