Institute for Medical Engineering and Science (IMES)

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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

ai, AI (Artificial Intelligence), Antibiotics, Artificial Intelligence, Bacteria, Biological engineering, Broad Institute, Collaboration, Computer science and technology, Disease, Drug development, Faculty, Harvard-MIT Health Sciences and Technology, Health sciences and technology, Institute for Medical Engineering and Science (IMES), Interview, Jameel Clinic, Microbes, School of Engineering, Synthetic biology

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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Charting the future of AI, from safer answers to faster thinking

Algorithms, Artificial Intelligence, Classes and programs, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Graduate, postdoctoral, Institute for Medical Engineering and Science (IMES), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, Students

Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are utilizing state-of-the-art resources, alleviating AI pain points, and creating new features and

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