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A new study published in the American Association for the Advancement of Science (AAAS) reveals that machine learning can make low-power MRI scanners more affordable and safer without compromising accuracy. This innovation opens up possibilities for patient-centric ultra-low-field (ULF) MRI scanners that utilize deep learning technology to cater to diverse healthcare needs globally. Despite the noninvasive and radiation-free benefits of MRI scans, accessibility remains a challenge in low- and middle-income countries due to the high costs and specialized infrastructure required for traditional MRI machines.
The development of a low-power ULF MRI scanner that operates on standard wall power outlets and eliminates the need for RF or magnetic shielding marks a significant advancement in MRI technology. By using a compact 0.05 Tesla magnet and incorporating active sensing and deep learning algorithms, the scanner achieves clear and detailed imaging comparable to high-power MRI devices. This breakthrough not only reduces power consumption significantly but also enhances the availability of MRI technology in smaller medical facilities.
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Sam Altman today revealed that OpenAI will release an open weight artificial intelligence model in the coming months. "We are excited to release a powerful new open-weight language model with reasoning in the coming months," Altman wrote on X.
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