A few days ago, Ping An’s doctors spent a huge sum of 3 billion yuan on artificial intelligence medical treatment , and they mainly focused on Chinese medicine treatment. Coincidentally, Keda Xunfei has also built the latest artificial intelligence hospital in Anhui. Looking back at the news of the past one or two months, the layout of artificial intelligence medical treatment is everywhere: In early August, Tencent released the artificial intelligence medical imaging product - "觅影" for early cancer diagnosis. In early July, Ali and Wanliyun officially released the "Doctor You" AI system, the main direction is medical imaging diagnosis. In July, Baidu announced an open computing platform. Artificial intelligence medical care has long been the target of all parties, and the technology advantage of the algorithm platform has established a strategy to become the leading giant. The reason for targeting the medical field is to look at the current situation of people's livelihood hotspots and pain points: the shortage of medical resources, the contradiction between doctors and patients, and the inequality of medical resources have long been the object of spit. From the point of view of artificial intelligence, after the continuous upgrade of technology, it is the key to find the landing scene, and the two parties will hit it off. From the initial less than 10 startups, to the present, there are hundreds or even thousands of artificial intelligence medical startups in the country. From the initial no one understands what artificial intelligence medical treatment does, why, and now the market capital begins. Actively chasing potential stocks, artificial intelligence medical companies may now ushered in their own spring. In the field of artificial intelligence, from the statistics, among the investment amount of artificial intelligence startups in the medical field, companies engaged in medical imaging and diagnosis, medical research, medical risk analysis and drug mining have absorbed more than 80% of the total investment. . In addition, medical robots have become another hot area. This is also evident from the robot conference held a few days ago. From the perspective of image management, it is mainly the identification and labeling of lesions, automatic delineation of target areas and adaptive radiotherapy and three-dimensional reconstruction of images. From these points of view, strictly speaking, it is not the field of artificial intelligence medical treatment, or mainly based on existing standards and medical knowledge for targeted search. From the perspective of machine learning, it is still difficult to independently discover specific images before the onset by computer, or to independently search for the rules of early features in the case of human cognition. The widespread use of medical robots is more reflected in the guide or welcoming role, even if the service is provided through voice interaction, it can not provide services like smart consultation or virtual doctor. Of course, this kind of service belongs to the higher stage of artificial intelligence, which is more difficult, and it faces the test of supervision, laws and regulations and social acceptance. It is really not a day's work. However, this also causes the temperature of the robot to begin to cool down, reminding the introductor to start paying attention to accumulating user data and doing deep excavation. However, we should be optimistic about the future of the above two businesses. For example, medical image data is the most common data in medical treatment, and the amount of data is extremely large. If you can dig deeper into the story behind the image data, it will be of great benefit to patient treatment and future health management. When it comes to data, it also touches on the essence of artificial intelligence. After all, only through big data can we train computer self-learning to find the best solution to break through the existing cognition, or provide humans with services to predict the future, and prepare the plan ahead of time. . Looking at the ideal goal of the future, there is still a certain gap between the current artificial intelligence intruders. Looking at the majority of market players, most artificial intelligence medical companies are cutting from a certain part. In particular, many companies are transformed from Internet medical companies. The collection of medical data is mostly for users to upload, relying on the previous user groups to support artificial intelligence. Data mining. This directly leads to a small amount of data, and is mostly a single data in a certain field, the content is more limited, it is difficult to carry out real deep mining and application service development. The driving force of artificial intelligence medical companies is technology. The key to long-term development is not the traffic that was valued in the Internet era. Instead, it focuses on accumulating data in combination with the characteristics of the artificial intelligence era, so that deep mining can be used to make big data play the role of predicting the future. Relying on the continuous accumulation of data and continuous optimization of algorithm models, for artificial intelligence medical treatment, in the future, after accumulating data, it can be expanded from the perspective of drug effect analysis and health data management. After technical support, consider the application of artificial intelligence medical treatment. service. Accumulating user data is the foundation. From the perspective of strategic layout, this helps artificial intelligence medical companies to fully target the top-level design, starting from the pain point of the medical market and considering the business layout. In view of the problem of insufficient data volume and single data content, patient data should be obtained from multiple parties. In addition to public hospitals, it can also be obtained through cooperation with medical examination centers and research institutions. Secondly, it is necessary to open up market-oriented channels with insurance companies, research institutions, enterprises, etc., and use the results of artificial intelligence mining for the general public, and use the B-end to contact the C-end. Finally, there is still a need to continuously improve the level of technical support, optimize algorithms and models, and allow artificial intelligence to provide more future forecasting services.
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Artificial intelligence medical layout is more than just robotics and intelligent image management in the future.