Recently, the white paper on the new generation of artificial intelligence (2020) – industrial intelligence upgrading, jointly prepared by the China Institute of electronics, China digital economy 100 people’s Association and Shangtang Intelligent Industry Research Institute, was officially released.
At present, the new generation of artificial intelligence is deeply integrated with all fields and links of the industry, accelerating the promotion of data and knowledge as the first element of economic growth, man-machine collaboration as the mainstream mode of production and service, cross-border integration as an important development mode, CO creation and sharing as the basic feature of economic ecology, continuously leading the industry to the high end of the value chain, and accelerating the upgrading of industrial intelligence. Capital pays close attention to the implementation of the new generation of artificial intelligence. According to the white paper, the intelligent upgrading of the medical industry from 2014 to 2019 attracted us $4.9 billion.
For the intelligent upgrading of the medical industry, the white paper analyzes the upgrading path, upgrading effect and market prospect, and shows relevant cases:
1. Upgrade path: a useful supplement to industry experience
By combining with wearable devices, the new generation of artificial intelligence technology takes the lead in the application of life-oriented health management, quantifies multiple health indicators of users in the form of data, and establishes a personalized health management scheme. Through speech recognition, natural language processing and other technologies, the patient’s disease description is compared with the standard medical guidelines, providing medical consultation, self diagnosis, guidance and other services independently. At the same time, the doctor’s oral doctor’s orders are formed into a structured electronic medical record according to the patient’s basic information, examination history, medical history, examination results and other forms. With the accelerated integration of deep learning algorithm and medical industry data, focusing on a large number of pathological cases in the medical field in the past, using machine vision, knowledge atlas and other technical means, through a large number of image data and diagnostic data, simulate the thinking, diagnostic reasoning and treatment process of medical experts.
2. Upgrading effect: promote the upgrading of high-quality medical resources supply
With the continuous upgrading and application promotion of the intelligence of the medical industry, patients can read their own accurate medical information data in real time with the help of smart devices such as portable wearable devices at home, and transmit the data to medical institutions through the intelligent diagnosis cloud service platform. Doctors make disease diagnosis and formulate supporting treatment plans according to the patient’s accurate medical information, which greatly improves the patient’s treatment experience, It has greatly improved the cure rate and alleviated the shortage of hardware resources in high-quality medical institutions. At the same time, with the further opening of industry data, the intelligent diagnosis and treatment system, combined with high-quality expert experience, assists grass-roots medical institutions to improve the detection rate of disease characterization, reduce missed diagnosis and help patients with major diseases such as cancer achieve early diagnosis and treatment, and effectively improve the diagnosis and treatment level of grass-roots medical institutions.
3. Market prospect: leading the medical industry into a new stage of prevention and universal benefit
Based on the characteristics of prevention, recuperation and personalized management, intelligent health management has gradually become the mainstream of preventive medicine. According to the global health risk report released by the World Health Organization in 2019, the market scale of personal health management will reach US $10trillion in 2025. With the collection of a large number of people’s health management files, the human health database will be gradually built in the future, and the health gene model will be trained by relying on the deep learning algorithm, Realize the prospective management of large-scale infectious diseases and chronic diseases. The gradually improved electronic medical record combined with intelligent diagnosis and treatment system is assisting doctors to improve diagnosis and treatment efficiency. According to the survey data of IDC, the intelligent diagnosis and treatment system has shortened the diagnosis process in the past 4-6 hours to 10 minutes, and the accuracy has increased to 91%. With the penetration of knowledge map, natural language processing, swarm intelligence, human-computer interaction and other technologies, The intelligent diagnosis and treatment system based on massive medical data and professional literature analysis gradually replaces some functions of doctors through hypothesis cognition and large-scale empirical analysis, realizes the integration from disease diagnosis to condition establishment to treatment plan formulation, and comprehensively promotes the universal benefit of high-quality medical resources.
Case 1: GE Healthcare launched Edison platform to empower medical institutions
GE Healthcare, a subsidiary of General Electric, has launched a fully integrated intelligent medical application development platform, edisonintelligenceplatform, which integrates global and diversified data from different business departments, suppliers, medical networks and life science application scenarios, and provides operational guidance that can be deployed on medical devices through cloud or device edge services. Around Edison platform, a number of intelligent medical applications have been developed, such as radiology command center scheme, logiqe20 dual engine ultrasound, CT intelligent subscription, imaging protocol and sequence center management platform of imaging department, mural intensive care command center, etc., to help medical institutions improve diagnosis, treatment and operation efficiency worldwide.
Case 2: Philips healthcare reshapes the health care cycle
Philips Medical Technology of the Netherlands uses in-depth learning technology to match, understand and adapt to changing health care needs, and supports clinicians by providing effective visualization, detecting deviations from normality and early warning to help doctors make correct decisions. At the same time, by building a data scientist platform, we can create and access high-quality health data sources, from helping health care providers to helping users manage their own health, so as to achieve the purpose of home care and health management.
Case 3: Siemens Healthcare creates a “chain” of smart images
Siemens Medical Company of Germany used knowledge maps to organize and deeply study image medicine, launched alpha anatomy engine and alpha report engine, realized fast anatomy based on pre-processing technology, and preliminarily combined multiple processing software and multiple software results to synthesize a report, which liberated doctors’ time and energy, and enabled doctors to pay more attention to the focus and syndrome itself, In addition, RadLex, a radiation language library, is constructed to realize bookmark retrieval for disease and syndrome, so as to provide in-depth mining in scientific research.
Focusing on how to continue to promote the intelligent upgrading of the medical industry, the white paper puts forward four measures and suggestions:
First, actively build new intelligent infrastructure. Including the construction of new network infrastructure, the construction and application of data intelligence infrastructure, and the construction of intelligent application infrastructure. Through these constructions, we will achieve accurate coverage of key regions and typical application scenarios, strengthen the ability of intelligent computing services, build an industrial intelligent innovation application system that spans levels, regions, systems, departments and businesses, and comprehensively improve the basic ability of industrial intelligent applications.
Second, accelerate the opening and financing of industry data. Establish and improve data sharing and disclosure system, strengthen data resource mining, analysis and application, and improve data security guarantee capability.
Third, vigorously cultivate industrial intelligent operation system. Including strengthening the cultivation of industrial intelligent development platform and carrying out application demonstration in key fields of intelligent upgrading. Leading enterprises are encouraged to deeply integrate industry data with the new generation of artificial intelligence technologies and algorithms to provide basic resource support and application development platform for industrial intelligent upgrading.
Fourth, optimize the construction of an intelligent security system. Strengthen the intelligent security of cloud computing and big data platforms, intelligent terminals and mobile applications. Strengthen safety protection, formulate industry standards, and strengthen evaluation and supervision.
Editor in charge: CC