According to relevant data, from 2013 to 2017, the entire AI+ medical industry received a total of 241 domestic financing. At present, domestic capital has multiple layouts of virtual assistants, medical imaging, medical robots, and intelligent health management. Among them, medical imaging has become a capital-intensive position, accounting for the highest proportion of 31%, ranking first. The attention is high, but there are many doubts. In addition to the underlying technology and data issues, the prospect of commercialization is also a key point for people not optimistic about medical AI. What kind of logic is used to dig more application scenarios, many artificial intelligence companies are still in the exploration stage. Among them, Airdoc Vice President Zhang Jinglei believes that under the influence of technical genes, cross-border is the first difficulty faced by many artificial intelligence companies. Therefore, the way of thinking must be changed; in addition, AI medical companies need to change existing medical care. The value chain, this change means optimization, making it simpler or cheaper. Outside the hospital, the main attack screening As a participant in the industry, Airdoc has gradually found a path suitable for its own development through research and practice on AI technology and medical health. Using ophthalmology as a vertical deep-plowing field, the artificial intelligence chronic disease recognition system has been developed. Through the Airdoc retina recognition algorithm, more than 30 chronic diseases, including diabetes, hypertension, arteriosclerosis, optic nerve disease and other chronic diseases and complications can be identified. Common eye diseases such as myopia and age-related macular degeneration. Zhang Jinglei said that this is mainly based on Airdoc's development strategy: focusing on the scene outside the hospital and focusing on medical screening. In the medical field, between the patient and the doctor, from diagnosis, treatment to disease management, each link represents a corresponding interest chain. However, the value chain in the medical industry has developed relatively mature after long-term development. Focusing on the outside of the hospital, it does not change the overall value chain, improve overall service efficiency, but also lower the threshold for companies to explore diversified business models. At present, Airdoc has cooperated with groups such as Xingchuang Vision to empower optical shops to reduce the total expenditure on medical insurance through grading medical treatment. Through these out-of-hospital cooperation, Airdoc's own data volume will also be added to provide preliminary data support for future chronic disease screening and disease mapping analysis. In terms of medical screening, Airdoc starts with a common disease with a high awareness rate and a disease with a corresponding solution. For younger users, Airdoc focuses on eye diseases and screening, focusing on eye diseases. For elderly users, Airdoc will be deployed to the grassroots and community in the future, focusing on chronic diseases such as arteriosclerosis and diabetes. Empowering the grassroots, doing chronic disease management Whether it is toB or toC, medical services are ultimately oriented to the C-end population. "We are optimistic about the future application of AI medical care at the grassroots level. Algorithms or AI must fall to the grassroots level in the future. For the grassroots, this has two meanings, screening and management." Zhang Jinglei told Hunting.com. Taking chronic disease management as an example, China currently has only 112 million people with diabetes, and 3-4 billion people have potential high blood sugar risk. Coupled with over 100 million patients with high blood pressure, cardiovascular disease, ophthalmology and high-risk groups, they are in urgent need of a simple, non-invasive, low-cost way to complete screening for multiple types of chronic diseases. These diseases could have been detected early through professional screening, but at this stage, there are 20% of county-level hospitals in China without ophthalmology. The number of ophthalmologists in the country is only 36,000, and the disease information can be accurately read from the fundus photographs. Professional doctors are only 200-300 people. “About 80% of people over the age of 50 have sub-health conditions. For these people, most of them already know that they have chronic diseases. From diagnosis to treatment, there is a chronic disease management cycle of 20-30 years. In this cycle, artificial intelligence will have great significance." Zhang Jinglei said. Taking diabetic patients as an example, according to the current treatment guidelines, the patient's diabetes management can be tracked by taking a fundus for 3 months and taking 4 times a year. Compared with measuring blood sugar, it has the advantage of low cost and quickness. At the same time, doctors and patients can receive relevant test reports, and more intuitively educate patients. For doctors, they can also adjust treatment plans based on more objective and diversified data. In the past three years, the Airdoc Artificial Intelligence Chronic Disease Identification System has launched screening for chronic diseases in large populations in Beijing, Shanghai, Hainan, Jiangsu, Hebei, and Anhui provinces, providing quick identification advice to tens of thousands of people every day. In 2017, the system has completed the identification of 2 million images. At the recent Vision China conference, the use of Airdoc's AI algorithm and ophthalmologists also confirmed the practicality of primary medical institutions plus artificial intelligence. With the support of national policy for AI medical treatment, at the beginning of this year, AIrdoc has sent the first three types of medical AI servers in China to CFDA. According to the medical device registration process, products must be finalized and tested from the declaration to the final review. Six steps including clinical trials, registration, technical review, and administrative approval. Zhang Jinglei said that it has entered the clinical trial stage, but it is estimated that it will take 3-5 years to pass the approval. There is no precedent and standard for how to design an artificial intelligence clinical trial report, so it will be of great significance. 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