Institute for Medical Engineering and Science (IMES)

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Justin Solomon appointed associate dean of engineering education

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Education, teaching, academics, Electrical engineering and computer science (EECS), Faculty, Institute for Medical Engineering and Science (IMES), Leadership, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, School of Engineering

Justin Solomon, associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), has been appointed associate dean of engineering education in the MIT School of Engineering, effective July 1.In this new role, Solomon will focus on advancing innovation in engineering education across the school. He will help shape new pedagogical approaches in […]

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Beacon Biosignals is mapping the brain during sleep

ai, AI (Artificial Intelligence), Alumni/ae, Alzheimer's, Artificial Intelligence, Brain and cognitive sciences, Disease, Harvard-MIT Health Sciences and Technology, Health care, Health sciences and technology, Innovation and Entrepreneurship (I&E), Institute for Medical Engineering and Science (IMES), Machine Learning, Mental health, Neuroscience, Parkinson's, School of Engineering, School of Science, Startups

The human brain remains one of the most fascinating and perplexing mysteries in medicine. Scientists still struggle to match neurological activity with brain function and detect problems early, slowing efforts to treat neurological disorders and other diseases.Beacon Biosignals is working to make sense of the brain by monitoring its activity while people sleep. The company,

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How to create “humble” AI

ai, AI (Artificial Intelligence), Artificial Intelligence, data, Health care, Institute for Medical Engineering and Science (IMES), Medicine, Research, School of Engineering, Technology and society

Artificial intelligence holds promise for helping doctors diagnose patients and personalize treatment options. However, an international group of scientists led by MIT cautions that AI systems, as currently designed, carry the risk of steering doctors in the wrong direction because they may overconfidently make incorrect decisions.One way to prevent these mistakes is to program AI

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3 Questions: Building predictive models to characterize tumor progression

ai, AI (Artificial Intelligence), Artificial Intelligence, Biology, Cancer, Computational biology, DNA, Evolution, Faculty, Genetics, Health sciences and technology, Institute for Medical Engineering and Science (IMES), Interview, Koch Institute, Machine Learning, Medicine, School of Science

Just as Darwin’s finches evolved in response to natural selection in order to endure, the cells that make up a cancerous tumor similarly counter selective pressures in order to survive, evolve, and spread. Tumors are, in fact, complex sets of cells with their own unique structure and ability to change. Today, artificial Intelligence and machine learning

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Using synthetic biology and AI to address global antimicrobial resistance threat

ai, AI (Artificial Intelligence), Antibiotics, Artificial Intelligence, Biological engineering, Collaboration, Drug development, Health sciences and technology, Institute for Medical Engineering and Science (IMES), Jameel Clinic, Research, School of Engineering, Synthetic biology

James J. Collins, the Termeer Professor of Medical Engineering and Science at MIT and faculty co-lead of the Abdul Latif Jameel Clinic for Machine Learning in Health, is embarking on a multidisciplinary research project that applies synthetic biology and generative artificial intelligence to the growing global threat of antimicrobial resistance (AMR).The research project is sponsored

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AI algorithm enables tracking of vital white matter pathways

ai, AI (Artificial Intelligence), Algorithms, Alzheimer's, Artificial Intelligence, Brain and cognitive sciences, Department of Defense (DoD), Health care, Health sciences and technology, Imaging, Institute for Medical Engineering and Science (IMES), National Institutes of Health (NIH), Neuroscience, Parkinson's, Picower Institute, Research, School of Engineering, School of Science

The signals that drive many of the brain and body’s most essential functions — consciousness, sleep, breathing, heart rate, and motion — course through bundles of “white matter” fibers in the brainstem, but imaging systems so far have been unable to finely resolve these crucial neural cables. That has left researchers and doctors with little

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3 Questions: Using AI to accelerate the discovery and design of therapeutic drugs

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In the pursuit of solutions to complex global challenges including disease, energy demands, and climate change, scientific researchers, including at MIT, have turned to artificial intelligence, and to quantitative analysis and modeling, to design and construct engineered cells with novel properties. The engineered cells can be programmed to become new therapeutics — battling, and perhaps

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Why it’s critical to move beyond overly aggregated machine-learning metrics

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Health care, Institute for Medical Engineering and Science (IMES), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering, Technology and society

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to test whenever a model is deployed in a new setting.“We demonstrate that even when you train models on large amounts of data, and choose the

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MIT scientists investigate memorization risk in the age of clinical AI

ai, AI (Artificial Intelligence), Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, cybersecurity, Electrical engineering and computer science (EECS), ethics, Health care, Health sciences and technology, Human-computer interaction, Institute for Medical Engineering and Science (IMES), Jameel Clinic, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Privacy, Public health, Research, School of Engineering, Technology and society

What is patient privacy for? The Hippocratic Oath, thought to be one of the earliest and most widely known medical ethics texts in the world, reads: “Whatever I see or hear in the lives of my patients, whether in connection with my professional practice or not, which ought not to be spoken of outside, I

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Researchers discover a shortcoming that makes LLMs less reliable

Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), Human-computer interaction, Institute for Medical Engineering and Science (IMES), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, Technology and society

Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study.Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail unexpectedly when deployed on new tasks.The researchers found that models can mistakenly link certain

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