Using synthetic biology and AI to address global antimicrobial resistance threat
Driven by overuse and misuse of antibiotics, drug-resistant infections are on the rise, while development of new antibacterial tools has slowed.
Driven by overuse and misuse of antibiotics, drug-resistant infections are on the rise, while development of new antibacterial tools has slowed.
Opening a new window on the brainstem, a new tool reliably and finely resolves distinct nerve bundles in live diffusion MRI scans, revealing signs of injury or disease.
Read MoreProfessor James Collins discusses how collaboration has been central to his research into combining computational predictions with new experimental platforms.
Read MoreNew research detects hidden evidence of mistaken correlations — and provides a method to improve accuracy.
Read MoreNew research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
Read MoreLarge language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
Read MoreMIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.
Read MoreThe team used two different AI approaches to design novel antibiotics, including one that showed promise against MRSA.
Read MoreCellLENS reveals hidden patterns in cell behavior within tissues, offering deeper insights into cell heterogeneity — vital for advancing cancer immunotherapy.
Read MoreThe Language/AI Incubator, an MIT Human Insight Collaborative project, is investigating how AI can improve communications among patients and practitioners.
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