"If we use the effectiveness of algorithms and data as the abscissa, our country is still in the primary stage, and we have not finished the data integration in the first stage." Today, Academician of the Chinese Academy of Engineering, Director of the National Cardiovascular Center, Chinese Academy of Medical Sciences Hu Shengshou, Dean of Fuwai Hospital, delivered a speech on the theme of “Smart Healthcare – Close to Us and Far from It†at the 2018 Shenzhen International BT Leaders Summit. Wisdom medical enthusiasm is high, but it’s hard to land. In May 2017, Alpha GoMaster came out and defeated our country's top nine-segment player Ke Jie by 3:0 to let people know the wisdom of computers. Subsequently, the words "artificial intelligence" and "big data" began to get hot. From the medical field, before 2015, “ mobile medical care †was a hot topic, but after 2015, people talked about the topic has been replaced by digital medical care . In the past one or two years, “Al+Medical†has become a popular one. hot word. Why is artificial intelligence going to fire? In Hu Shengshou's view, this is inseparable from the efforts of the state, enterprises and universities. At the national level, in July 2017, the State Council issued the “New Generation Artificial Intelligence Development Planâ€, proposing that by 2020, the competitiveness of the artificial intelligence industry will enter the international first phalanx; and it is impossible for enterprises to miss this wave. The BAT giants are accelerating the layout of artificial intelligence; in colleges and universities, Peking University, Tsinghua University, and Nanjing have established artificial intelligence majors, and artificial intelligence has even entered the middle school textbooks. Under the wave of artificial intelligence booming, artificial intelligence technology has also made a lot of progress in the medical field. Hu Shengshou, for example, said that Keda Xunfei Zhizhi helped to participate in the 2017 national qualification examination for medical practitioners to enter the top 5% of the country, in the Tiantan Hospital. In the war, AI also won by 20% higher accuracy, and the US FDA has approved the entry of 12 pan-AI medical products into clinical applications. The degree of wisdom in smart medical care is self-evident. According to relevant data, the size of the smart medical market is expected to exceed 20 billion in 2018. In addition, in June this year, Yiou held a “smart + big health†summit in Shanghai. On the day of the summit, the audience was full, and everyone’s focus on smart medical care was also visible. "Wisdom medical thunder and heavy rain is small, landing is very difficult." Hu Shengshou said. At present, there is no real smart medical product in China that has passed FDA certification. The reason why landing is very difficult is mainly related to the effectiveness of medical data. Data validity is still the "roadblock" of smart healthcare According to Hu Shengshou, data, computing power and algorithms are the troika of smart medical care. Artificial intelligence is inseparable from the deep learning of the machine. Deep learning requires marking a large amount of data and training tens of thousands of pictures to make a correct diagnosis. It is understood that by 2020, the total amount of medical data generated by humans will reach 40 trillion GB. However, the data is not equal to big data. In China, there are mainly incomplete and unreal problems in data. For example, the diagnostic behavior is not standardized, and the information is generated and collected. "I am a cardiac surgeon and the dean of a hospital. We often check electronic cases. Many hospitals in the country, very well-known hospitals, check out outpatient cases, the pass rate can reach 50% to 60%. It is quite good, often appears. The hospital made a mistake in gender. For example, many hospitals say that men have a history of menstruation," Hu Shengshou said. Once there is a problem at the source of the data, it will affect the diagnosis of the disease in the later stage of Al. This is why China has not yet passed the FDA certification for smart medical products. On October 21, 2016, December 12, 2017, the National Health and Family Planning Commission identified two batches of health care big data center pilot provinces and cities, including: Fuzhou, Xiamen, Nanjing, Changzhou and Shandong, Anhui and Guizhou. The purpose of determining the medical big data center is to promote the standardization, structuring and standardization of data from the bottom. This is the foundation of smart healthcare. In addition, Hu Shengshou said that in the process of deep learning, the computer needs someone to participate and label the cases, so the process is also very long. Smart medical care to boost the ability of primary medical services There is no doubt that medical artificial intelligence still has a long way to go in China. Hu Shengshou believes that China is still in the stage of data integration, and the United States has entered the second stage, data sharing and perceived intelligence. The third stage is cognitive intelligence + health big data, forming a medical health industry based on the clinical application of big data smart medical products. The development of smart medical care has a long way to go, but why do departments at all levels still vigorously promote this? In Hu Shengshou's view, smart medical care has greatly helped to improve the service capacity of primary health care. At present, the biggest problem in China's medical care is the uneven allocation of medical resources, the large hospitals are overcrowded, and the grassroots hospitals are in front of them. The main reason is that the level of primary medical care is not enough, and the ability of grassroots doctors to practice is insufficient. In Hu Shengshou's view, a smart doctor system based on healthy big data can solve this problem. Taking high blood pressure patients as an example, there are more than 3 billion hypertensive patients in China. If you develop a 1.0 version of a hypertensive doctor, with Internet + artificial intelligence technology, it can treat two or three thousand high blood pressure drugs. Data, patient personal information, family information, genetic background factor data are collected, and based on algorithms to help doctors more accurately diagnose the patient's condition. "To solve the core problem of artificial intelligence, we need to do the basic work with peace of mind and solid foundation, so that the data is accurate, complete and structured so that our machines can be read and the correct data can be generated correctly. 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