Experts and academicians have a total of words: How far are we from the era of intelligent medicine?
On December 18th, the “New Generation Artificial Intelligence Academician Summit Forum – Smart Medical Forum†jointly held by Pengcheng Lab and a new generation of artificial intelligence industry technology innovation strategic alliance was held in Shenzhen. At the meeting, Liu Rong, a central health care expert, Yu Yizhou, the chief scientist of Shen Rui Medical, and Zheng Hairong, deputy dean of the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, shared and discussed topics such as intelligent medicine and intelligent image-assisted diagnosis. Al has caused great changes, and the era of intelligent medicine will come Medical treatment is one of the better landing scenarios for artificial intelligence. In recent years, with the introduction of artificial intelligence policies and the influx of capital, medical artificial intelligence companies have sprung up. At present, artificial intelligence has been applied in all medical industry chains such as medical imaging, hospital management, health management, and drug mining. In the view of Liu Rong, a central health care expert, the future will enter the era of intelligent medicine. Intelligent medicine is not simply equivalent to the smart medical care that people are talking about now. Smart medical care only focuses on the new artificial intelligence products that are driven by artificial intelligence technology. Intelligent medicine refers to tools and methods that use artificial intelligence to assist or replace The science of human conduct for medical behavior. The emergence of intelligent medicine is mainly due to the advancement of artificial intelligence technologies such as speech recognition, image recognition, video recognition, and intelligent navigation. Taking image recognition as an example, the advancement of this technology has promoted the emergence of smart medicine from three aspects: First, it has promoted the development of medical imaging diagnosis. At present, the accuracy of image diagnosis of skin cancer has exceeded 91%; second, multimodal image recognition can be performed. By integrating fusion analysis of multimodal medical images such as CT, nuclear magnetic and molecular imaging to extract more image information, the diagnostic accuracy can be greatly improved. Thirdly, three-dimensional reconstruction can be performed to enable doctors to analyze medical care more intuitively. data. What other imagination space does the era of intelligent medicine have? Liu Rong explained the surgery as an example. In the future, the surgery will use surgical robots for surgery, and will also include intelligent navigation and automatic control. In Liu Rong’s view, there will be some problems in the era of intelligent medicine that need to be resolved. For example, in the future, doctors will change from operators to supervisors, and the role of doctors will also pose challenges. In addition, data security will also become a prominent issue. New opportunities for artificial intelligence in medical imaging According to the "2018 China Medical Artificial Intelligence Development Research Report" released by the billion-dollar think tank, intelligent image-assisted diagnosis companies have the largest number of investments in the past few years. Capital injection, on the one hand, shows that there is a lot of imagination in the field of intelligent aided diagnosis; on the other hand, it has also contributed to technological progress in this field. Zheng Hairong, deputy dean of the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, used the detection of atherosclerotic plaque in the brain as an example to clarify the role of artificial intelligence in the image data collection stage. In the image data collection phase, magnetic resonance imaging is a reliable means of plaque vulnerability assessment. However, at present, because the detection time of magnetic resonance imaging is too long (generally takes more than 10 minutes), it is prone to motion artifacts, which leads to errors in the diagnosis results. Through the three-dimensional fast spin echo and cornering algorithm in artificial intelligence technology, the time of magnetic resonance imaging detection can be effectively shortened to less than 5 minutes, and the plaque feature assisted intelligent analysis is improved. In the image data analysis phase, combined with Tencent Al's screening study in digestive endoscopy, Tencent senior researcher Sun Zhongqian suggested that gastric cancer can be screened by using knowledge and data from esophageal cancer screening. This is a migration learning between different diseases, which can make the intelligent diagnosis model more optimized. However, according to Zheng Hairong, the problems in the field of intelligent image-assisted diagnosis are still standardization of data collection and standardization of data annotation. In data collection, the inconsistency of imaging parameters and equipment will affect the standard of data; in the data labeling stage, doctor qualifications, labeling methods and different doctors' inconsistencies in pathology will lead to inaccurate data labeling.
Introduction:
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