Physics and Its Contemporary Role in Health, Medical Sciences and its Connection to Digital Technologies and Artificial Intelligence
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The present advancement of health and medical sciences has been greatly aided by the fundamental pillar of physics. It has supplied the scientific and practical underpinnings for comprehending biological events and precisely identifying and curing illnesses. Medical imaging technologies including computed tomography (CT), magnetic resonance imaging (MRI), nuclear medicine, and X-rays have been made possible by the concepts of radiation and medical physics, allowing doctors to identify illnesses more quickly and accurately. By precisely controlling radiation doses and protecting healthy tissues, physics has also improved treatment techniques, especially in radiotherapy and cancer treatment. Additionally, biophysics has been essential in comprehending how organs and biological systems work, including respiratory mechanics, blood flow, and nerve signal transmission. In the current era, physics has been connected to digital technology and artificial intelligence, which has made it easier to create intelligent medical devices, physical simulation models for disease behavior prediction, and higher-quality healthcare. Therefore, physics plays a crucial role in attaining medical breakthroughs, improving the effectiveness of diagnosis and therapy, and fostering innovation in the modern health sciences.
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[24]
X. L. Y. W. T. T. M. T. L. C. W. L. T. Y. X. Dong, "Automatic multiorgan segmentation in thorax CT
images using U-net-GAN," Med. Phys., vol. 46, no. 5, pp. 2157-68, 2019.
O. F. P. B. T. Ronneberger, "U-net: convolutional networks for biomedical image segmentation," in
Internaltional conference on medical image computing and computer-assisted intervention
(MICCAI), Cham, 2015.
[24]
X. L. Y. W. T. T. M. T. L. C. W. L. T. Y. X. Dong, "Automatic multiorgan segmentation in thorax CT
images using U-net-GAN," Med. Phys., vol. 46, no. 5, pp. 2157-68, 2019.
O. F. P. B. T. Ronneberger, "U-net: convolutional networks for biomedical image segmentation," in
Internaltional conference on medical image computing and computer-assisted intervention
(MICCAI), Cham, 2015.
[24]
X. L. Y. W. T. T. M. T. L. C. W. L. T. Y. X. Dong, "Automatic multiorgan segmentation in thorax CT
images using U-net-GAN," Med. Phys., vol. 46, no. 5, pp. 2157-68, 2019.
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