On June 15, 2018, under the guidance of the Shanghai Municipal Commission of Economy and Information Technology, the Shanghai Municipal Commission of Commerce, and the Shanghai Changning District People’s Government, the "2018 Global Intelligence + New Business" was jointly undertaken by the Shanghai Changning District Youth Federation and Yiou Company. The Summit-Smart + Health Summit" was successfully held in Shanghai Changning World Trade Exhibition Hall.
This summit took AI and medical care as the entry point, and carried out a full and in-depth discussion on AI-enabled medical care around several major themes such as digital life, smart medical care, genetic testing, AI imaging, health management, and hospital management. The guests attending this summit included Ma Jun, Dean of Shanghai Tongren Hospital, Kang Rong, Vice President of Microsoft Greater China, Wang Xi, Vice President and CTO of Philips China, Zhong Xin, Founder and CEO of Touma Shenwei, and Founder of Voxel Technology Person and CEO Ding Xiaowei, Zhang Chuntang, Vice President of Marketing Technology, Li Chaoyang, Vice President of Shenrui Medical Market, Zhao Nan, Co-founder and CSO of Jellyfish Gene, Li Yuxin, Founder of Health Benefit, Sun Qi, Founding Managing Partner of Daotong Investment, Vice President of Yiou Company President Gao Ang, Vice President of Yiou Company and Dean of Yiou Think Tank Research Institute You Tianyu, etc.
At the conference, Zhong Xin, founder and CEO of Tuma Shenwei, delivered a keynote speech on "Let Deep Learning Enter Smart Healthcare".
The following is a shorthand for the live speech:
Hello everyone, I am Zhong Xin from Tuma Shenwei and the founder of the company. I am very honored to be here today to share with you all of our company's experience of artificial intelligence medical imaging. The topic of my speech today is to let deep learning enter smart medicine.
Combination of Al and medicalSpeaking of smart medical care, which applications are more intelligent, we talk about disease prevention, which is a very important field, and auxiliary diagnosis and treatment is also a very broad direction. The processing of medical images we do is a very hot and very large application, including our health management, including our diet, daily habits, and these should be placed in the direction of smart medical care.
AI combined with these directions can be applied in multiple scenarios, such as the auxiliary medical research platform, which contains the speech processing, semantic processing, and natural language processing just mentioned. It can be applied to the hospital's EM2 system. This management can be Make convenient structured reports. Another example is health management and nutrition. The heat energy and nutrient content of each food can be automatically analyzed through artificial intelligence, and it will remind us of our daily intake. Drug mining is brand new, as well as drug prediction. Auxiliary diagnosis and treatment, like our company is a company that does auxiliary medical diagnosis and treatment plans. Medical imaging virtual doctor assistants, including nurses at home, can monitor our daily physiological indicators in real time.
Talk about the development of the international Al medical industry. There are probably more than 100 companies in the global AI medical company, of which there are more than 90 well-known startup companies in the world, and 20 or 30 companies are mainly concentrated in the direction of medical imaging, analysis and testing. For example: medical insurance companies have to do some analysis for their patients to determine what kind of insurance their patients are suitable for. These risks are very necessary. Speaking of pathology, we know that many companies, especially in the United States, are promoting or improving the digitalization of pathology, and pathology analysis is also a very large and urgent task.
Domestically, we know that the domestic AI medical industry has no fewer than 100 startup companies from 2016 to now. Among the more than 100 companies, the main direction of more than 70 companies is the analysis of medical images and the labeling of medical data. I think this direction is a very direct medical application; there is also the direction of radiotherapy, mainly radiology medical imaging. , There are also many companies doing AI research in this area, mainly for automatic planning of bullseye and adaptive diagnostic monitoring during radiotherapy. This is a very important direction; there is also three-dimensional reconstruction, thinking more about the analysis of medical images , How deep learning and AI analyze medical images can also help in imaging. For example, imaging quality control and algorithm selection can be achieved by deep learning. We know how many such companies Home, including doing ultrasound images and CT images in many directions.
We made a statistics of these three directions. We found that among domestic AI innovation companies, about 70%-75% of companies do medical image analysis. This is a very concentrated area, which is relatively scattered compared to foreign countries. The situation is different. The other 20% and 30% are in the two directions just mentioned.
The value of deep learning in the medical fieldFirst of all, doctors, doctors are the biggest users and our customers. Where is the greatest help to the doctor? First, deep learning can help low-skilled doctors or doctors in remote areas quickly upgrade to their seniority level. The second is the improvement of work efficiency. As a group of Chinese doctors, and hospitals as an industry, their productivity is not enough. Our AI can greatly improve the productivity of doctors in this regard.
Let's talk about patients. AI can report all diseases of patients to doctors in advance through automatic inspections, reminding doctors and patients to pay attention, so it is an accurate, efficient and personalized diagnosis and treatment plan to submit to patients. This is difficult to achieve without AI.
Let’s talk about hospitals, there are two biggest help to hospitals: First, increase efficiency; Second, reduce costs.
Pharmaceutical companies, everyone knows that using artificial intelligence technology for drug research and development can shorten its research and development cycle. Many oncology drugs have recently learned that there are many emerging companies in China, and there are more in the United States. These companies are using artificial intelligence and machine learning to develop drugs.
Gene sequencing has been about 10 years of development. In the last six or seven years, this technology has been very mature. Many machine learning artificial intelligence methods are used in this technology, especially machine learning methods. We analyze us through statistical methods. The reason why a certain group of people is prone to a certain type of disease is achieved by large-scale statistics.
For medical insurance companies, each radiotherapy patient needs 100,000 to 120,000 per year. If early patients are found through artificial intelligence, the disease can be completely cured in two years by performing surgery plus radiotherapy for 40,000 US dollars. Cure, this is to help medical insurance. At the same time, all of their insured patients can be analyzed through big data. Different patients' genes and his lifestyle can be analyzed. Whether he is suitable for accepting insurance and suitable What kind of insurance.
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