When will China’s smart medical robot “Watson” come?

In 2011, IBM released a super doctor assistant called "Watson". The medical materials of tens of millions of G were "irrigated" into the brain of "Watson", including the patient's clinical manifestations, laboratory tests, and treatment plans. Etc., can provide doctors with good disease diagnosis and treatment decision support. The "Watson" work process is actually a complete big data analysis process, which is the successful application of medical health big data. Natural language understanding, finding evidence, and judging are the three abilities of Watson. This "cognitive computing" ability makes "Watson" a useful place in the current wave of big data. “Watson” collects data from different sources of information and refines it, providing advice and advice to hospitals, or physicians and doctors on medical procurement. At the MD Anderson Cancer Center at the University of Texas, "Watson" advises doctors to treat complex diseases and cancer.

Domestic medical health big data is exploding. According to statistics, in the past two years, domestic capital investment in the medical industry has begun to consciously “embrace” Internet genes, and investment in software and hardware accounts for 38% of total Internet medical investment. The investment in mobile medical care accounts for 19%, and the ultimate goal is the occupation of healthy big data terminals. In such a market environment, can China produce smart medical robots like Watson?

Medical big data is surging, generating huge commercial and social value

On August 31, the "Notice of the State Council on Printing and Distributing the Action Plan for Promoting Big Data Development" proposed that a unified open platform for national government data be established by the end of 2018, and that public data resources should be rationally and appropriately implemented in important fields such as medical care, credit, and transportation. Openness will drive the public to carry out big data value-added, public welfare development and innovative applications, fully release data dividends, and stimulate public entrepreneurship and innovation. After the announcement, the hot topics such as medical big data have been warmed up again in recent years, and it has become one of the focuses of investment.

At the same time, in August, September and October, seminars on personalized medicine and precision medicine were held in Shanghai, Nanning, Xiamen and Wuhan. The policy background of this indication is that the National Health and Family Planning Commission has successively issued the "Technical Guidelines for Gene Detection of Drug Metabolizing Enzymes and Drug Targets (Trial)" and "Technical Guidelines for Individualized Treatment of Tumors (Trial)" to personalize medical treatment. And precision medicine has risen to the level of national policy, which has triggered a strong response from the industry.

In fact, in the first step of European and American countries, the development of medical big data is not new. In his report, McKinsey pointed out that by removing institutional barriers, big data analysis can help the US healthcare industry create $300 billion in added value a year.

Talking about specific medical big data application cases, IBM's "Watson" is a living case. According to PCWorld, US drug retailer CVS Health and IBM reached a partnership in July that will use predictive analytics and Watson cognitive computing to improve care management for patients with chronic conditions. CVS Health has 7,800 pharmacies across the United States, which will work with IBM to optimize co-developed technologies for use in a range of chronic conditions such as high blood pressure, heart disease, diabetes, asthma, obesity, and more. According to IBM Chief Health Officer Cocu Rey, the overall goal is to enable CVS pharmacists and healthcare providers to “better personalize, customize care for patients and improve patient health to the best possible outcome”. It is understood that the optimized technology will add the ability to identify the risk reduction of the customer's health and focus on getting the customer into the prevention program. The technology will also focus on encouraging patients to use prescription drugs, health regimens, and advice for community health and outpatient clinics.

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