Medical big data and remote

“I hope to work with Internet innovators to create a 'clinical genetic machine doctor' to solve the diagnostic problems of rare genetic diseases. Of course, this requires the participation of gene sequencing companies and clinical geneticists. As long as you input the collected Genotypes and phenotypes, 'Clinical Genetic Machine Doctor' can help you make an accurate diagnosis right away, and give the most reasonable advice and tips for the next step."

At the end of March this year, Duan Tao, the president of Shanghai First Maternal and Infant Health Hospital, published a dean's diary, "Disappointing Internet Innovation," in his personal WeChat subscription number "Dian Duo Dao". The above paragraph is the end of this article, and it is also a challenge put forward by Duan Tao. I hope that companies with innovative genes can complete this challenge.

On the same day, Dr. Zhang Rui, CEO of Chun Yu, expressed his position in his circle of friends: “The challenge of the dean, Chun Yu is coming!” Zhang Rui believes: “In the next three years, it is a good time for AR and biomedical engineering to aggregate and fission. Technology The innovation is the source innovation. Chunyu has been doing the shallow medical artificial intelligence of the 'human brain training computer'. It is applied in our user self-diagnosis and intelligent triage, and saves our labor cost by no less than 10 million per year. Now is the time to move forward towards a more professional medical field !"

This air-to-air dialogue demonstrates the vision of the most advanced minds in the medical field for the future use of medical big data .

Indeed, when AlphaGo shines, artificial intelligence does bring too much imagination to every field. Especially in the medical field, when the Watson robot's ability in the field of disease diagnosis can be comparable to that of a general practitioner, it may be worth more money, energy and passion to do more exploration.

Story version one

The basis of medical artificial intelligence is the mining and application of medical big data. Regarding medical big data, this is the most widely spread story in the Internet medical field in recent years. Although the model of each startup is very different, there is no exception: there is a mining and application of big data, although the part about how to implement is often unknown. This fully reflects two issues: everyone is aware of the value of medical big data development, but the path of development is very difficult.

So, the story of the first generation of medical big data we heard became like this:

1Related data source

China's Internet medical care can be said to be a self-made, and it is still not seen by the traditional medical system. Therefore, the early medical big data can basically only come from the accumulation of Internet medical companies themselves. The “early” here refers to both the early stage of time and the early stage of development thinking. Well, there are probably a few types of early data sources (welcome to add):

Online consulting companies - these companies are both integrated and vertical. The way of data accumulation is mainly to establish a patient's personal electronic health record through the online consultation of doctors and patients;

Intelligent hardware companies - purely intelligent hardware is increasingly used in the medical field, but more and more medical services are beginning to combine with intelligent hardware, such as blood sugar, blood pressure, body temperature, heart rate, etc. The data accumulation method is mainly for user physical data. Detection

Genetic testing companies – genetic testing has become increasingly popular in the past two years, mainly driven by lower testing costs and precision medicine , and the threshold has been greatly reduced, enabling more and more ordinary users to consume genetic testing.

Research tool companies—Although collecting disease data for patients like doctors and resources companies, the forms and applications of data collected by research companies are significantly different. Research institutions have played a leading role in the process of data accumulation.

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