Imagine: In the future, if AI treatment becomes a reality, you can see a doctor directly in the hospital. Through the test data, the system quickly gave you a diagnosis, let you go to prescribe medicine, even decide whether or not to surgery... Although the process sounds very convenient and beautiful, but to reach the utopia of AI clinic In fact, it is not so simple, and no matter whether its bricks are still under construction, it is not easy to imagine the road to Utopia.
Medicaltranscriptionsservice
▌ Stay in the lab for AI treatment
The user group of AI diagnosis and treatment is not limited to assistant doctors. According to different service groups, it can be divided into four levels:
• Respond to patient consultation;
• Auxiliary guide staff, pharmacy staff, medical online customer service;
• Clinically assisted decision-making by primary doctors and general practitioners;
• Patient rehabilitation tracking for doctors.
The above functions should be related to AI diagnosis and treatment. If a medical startup does not have a large amount of data, it is impossible to make a mature product.
In the case of a lack of data, the main way for startups is to work with hospitals or data centers to obtain relevant data. The main use of these data is not for the actual diagnosis of hospitals, but only the accumulation of the previous work - to optimize the algorithm through massive clinical data.
Jeremy Howard, founder of artificial intelligence diagnostic star Enlitic, once said that the lack of data is the main reason for their development. It received a $10 million grant from Capitol Health last year to improve diagnostic algorithms through radiology data provided by the latter. Through the mining of massive medical data, real-time and accurate diagnosis of medical images can be realized, the prediction of disease probability can be provided, and doctors can make decisions.
At present, the most successful AI treatment system is not IBM's Watson. According to Lin Xueting, a software engineer at Watson Health Cloud, the Tokyo Institute of Systems and Software Development, the system needs a research center that can collaborate to obtain accurate data sources. Watson's approach to data acquisition is to work with CVS, the second-largest chain pharmacy in the US, to obtain user behavior information, clinical data, drug purchase data, and insurance information, or to work with laboratories and research centers around the world.
However, the current research work is mainly limited to the laboratory. Even with the successful case of Watson's diagnosis of leukemia, it is still some distance away from clinical trials.
Hsnewsbeat
â–ŒData validity
The biggest flaw in AI diagnosis and treatment is the data collected for the medical research institute, and there is still a distance from the real consultation.
One reason is that the data size is small. At present, most of the technology companies' practices are to exchange customized solutions in order to obtain data from hospital institutions, but these clinical data can only be regarded as "small play".
Simply for everyone to count the bill: In the UK alone, there are nearly 200 people who can't see the British rain scene tomorrow (smoothly black). By 2020, there will be 200 million people worldwide blinded by AMD, a retinopathy of diabetes.
However, even if Google DeepMind and the British century-old hospital Moorfields such a strong team, the current training data can only reach more than 1 million anonymous scans. In contrast, the data provided by independent hospitals is a drop in the ocean compared to global patients. At the same time, data access to the disease is limited by geography and even disease, which makes the data more effective.
Another problem is that the quality of the data needs to be improved. Insufficient electronicization, data collection methods, and lack of standard systems and low-level data sources have made clinical trials more difficult.
Medical data is not as mature as financial data, and the degree of granularity and professionalism is relatively mature. At present, the degree of HIS and EMR in hospitals is far from enough. "The quality of data is the basis of effective analysis. At present, data cleaning work takes up too much work, and after all, it is still a quality problem." Zheng Jie, CEO of Shulan Hospital said. He believes that doctors who use hospital information systems are mostly older, have higher rejection of accepting the latest information systems, and do not have the urgent motivation to analyze data. Therefore, it is difficult to establish excellent data structures and data. Quality".
Lei Feng.com asked several doctors in first- and second-line hospitals. They said that the hospital did not introduce relevant artificial intelligence diagnostic facilities. Because the accuracy rate is not optimistic, it is still in a wait-and-see state, but it does not rule out that "when the regulations allow, Artificial intelligence diagnosis is used as an auxiliary diagnosis."
Kang Chaozi CEO Zhang Chao said, "The current diagnosis on the market (the expert system that has been done for many years) is mainly based on symptoms, and a few can add laboratory data, but in fact, medical history, medication, incentives, etc. need to be gradually learned. "The data of the test is more of a reference to the present, and the doctor's "expectation and inquiry" is a comprehensive consideration of multiple dimensions such as symptoms, incentives, medical history, and medication history. The poor quality of the data will inevitably hinder the learning of artificial intelligence.
In addition to quantity and quality, the lack of law also questions the validity of the data.
Regardless of the level of informationization of the quality of these data, not to mention that DeepMind has caused public opinion attacks because of an annual exchange of 1.6 million data with NHS. Apple also does not allow developers to store data on iCloud in the latest specifications. It is also a specification for technology companies to avoid the risks associated with leaking data.
Up to now, there is no corresponding AI diagnosis and treatment regulations at home and abroad, and there is no clear norm for the relevant responsible subjects and the treatment process. At present, foreign third parties can only use the statistical data of personal data according to the HIPPA agreement. The Medicare Carrying and Responsibility Act of 1996 is designed to protect the privacy and health-related electronic data of patients and to make the data exchange process as standardized as possible.
The technical guarantees defined by HIPAA's security principles do not require the use of a specific technology, but rather an adjustable framework that requires organizations to use as many of the appropriate technologies as possible to protect data security. "Check control, information integrity, data transmission, etc." A variety of requirements.
Medical data is often not shared on a large scale due to privacy concerns. However, people are naturally reluctant to “share†with others because of the natural rejection of their illnesses. The problem of “island of information†has intensified this situation.
Venturebeat
Summary:
Simply put, the main reason why AI diagnosis and treatment failed to develop rapidly is that the quantity and quality of data are not enough to support the real consultation at the current stage. Diagnosis and treatment is a very personal and personalized activity. In order to make the medical treatment data open and use on a large scale, in addition to the big data analysis on semantic-based natural language processing, legal support and protection are also needed.
In this way, medical big data can truly serve the exploration of artificial intelligence in terms of effectiveness, and provide help and support for AI diagnosis and treatment. Maybe in the near future, we can reach Utopia and enjoy the convenience of computer treatment.
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