The application of AI in the pharmaceutical industry is seen as a major export for artificial intelligence companies, and pharmaceutical companies are also considered to be the potential largest payers.
The arterial network has been counted. From the perspective of capital inflows, as of July this year, the new inflow of "AI + drugs" in the field of funds exceeded 600 million US dollars, more than last year, there has been a spurt of growth. More than 100 companies have been labeled "AI+ New Drugs" at home and abroad.
AI can be applied to many fields such as drug discovery, preclinical research, clinical trials, rational drug use decisions, pharmacovigilance, and drug recombination discovery.
On September 26th, at the "2018 Artificial Intelligence + Pharmaceutical Enterprise Innovation Forum" co-sponsored by the Arterial Network, Eggshell Research Institute, China High-tech Industrialization Research Association, China Medical Device Industry Association and Zhejiang Health Industry Federation Several representatives from medical and health related experts and scholars, medical and health innovation companies, investment companies and medical institutions shared their insights and discussed the value of AI for pharmaceutical companies and hospitals, and how to solve the challenges of breaking through AI applications.
Speaker Rosmeier is the Vice President of Technology Solutions and Innovation Labs at Medidata.
He brought a new data for everyone: in the 10 years from 2006 to 2015, the conventional method for the development of new drugs, the success rate from the first to the second is 63%, from the second to the third is 28.8%, 55% can enter the new drug application stage, but even if it enters this stage, only 83.9% can be finally approved. If these percentages are integrated, only 8.4% of all Phase I study drugs will be successful.
However, if AI is used to select biomarkers during the development process, the success rate of each of the above stages can be significantly increased, reaching 76.7%, 46.7%, 76.5%, and 94.5%, respectively. The combination of these percentages means that the success rate of the first-phase study drug can reach 25.9%, more than three times the original!
It is this amazing effect that pharmaceutical companies have joined forces with AI companies.
Medidata has more than 1,000 customers worldwide. On its software platform, more than 13,000 studies were conducted and 3.8 million patients could provide valuable data.
In China, Medidata has helped 870 clinical laboratory research, with 146 customers, including Haizheng Pharmaceutical, Fosun Pharma, WuXi PharmaTech and other leading pharmaceutical companies and contract research organizations (CROs).
Internationally, among pharmaceutical giants, GSK, Novartis, Johnson & Johnson and AI company Insilico medicine have cooperated, Merck and Abbvie are Atomwise and cooperation; AstraZeneca, Pfizer, Takeda Pharmaceutical and other pharmaceutical companies have also started with artificial intelligence companies. Cooperation.
Embrace AI, just need to be all the links
The penetration of artificial intelligence will reshape every industry, and in the medical field is no exception. Artificial intelligence and deep learning can greatly improve efficiency is no longer a gimmick, but a fact. At present, the application trends and patterns of AI are gradually becoming clear, and the value to all parties is beginning to become more prominent.
From the perspective of patients, artificial intelligence can better address their medical needs, provide better medical services, and is welcomed by patients. Fan Xiaolei, co-founder of Suowenbo, said: “Patients are increasingly accepting decisions based on big data and artificial intelligence, giving decisions and recommendations for treatment. Through the participation of new technologies in the treatment management process, patients are receiving more and more. From the data, our patients are very open and inclusive about the development of new technologies and treatment data, which is a happy conclusion."
For pharmaceutical companies, the most direct benefit of artificial intelligence for pharmaceutical companies lies in the development of new drugs. Mr. Li Yunfei, Director of IPD Management Office of Product Integration and Development of Tasly Pharmaceutical Group, said about the role of AI in discovering targets: “The constant understanding of diseases, the development of technology, the path of treatment, and the means of treatment are constantly changing. More and more complex, the technology needed is more and more high-end. Informatics, including the so-called big data and AI, will definitely play a role in the future, and it will play an important role."
In addition, Li Yunfei also said that in addition to being a tool for the development of new drugs, artificial intelligence can also be used as a tool to rediscover the value of traditional Chinese medicine. For example, network pharmacology is a tool that integrates technologies such as drug informatics, software information, molecular biology, big data, and artificial intelligence. It can predict the target of Chinese medicine, identify the active ingredient group of Chinese medicine, clarify the mechanism of action of Chinese medicine, explain the rationality of prescription and the prescription of traditional Chinese medicine, and help to find new indications.
In addition to pharmaceutical companies, artificial intelligence can also relieve the dilemma of the hospital. At the hospital end, hospitals at different levels have different needs, and artificial intelligence is one of the key technologies to solve problems. For the construction of medical associations in secondary hospitals, improve the level of diagnosis and treatment, and better serve the needs of patients. After the flow of big data, artificial intelligence and deep learning can better replicate the clinical experience, diagnosis and treatment process of higher-level hospitals, and empower primary health care workers. For the top three hospitals, a large number of doctors have a large amount of research needs, and artificial intelligence can make better use of real-world data.
Data problems are still a congenital deficiency of domestic AI
Although AI has broad application prospects, AI wants to achieve landing applications that require a large amount of standardized and structured data for "breeding." Second, all parties in medical health services also need to change their minds and actively embrace artificial intelligence.
In terms of data, if artificial intelligence is divided into three dimensions of algorithm, computing power and data, the main opportunities of the industry are now concentrated on the data and application level. The core of competition lies in the quality and quantity of data. However, for Chinese medical artificial intelligence companies, there are large-scale potential data in the market, but they cannot be sorted out and used. On the one hand, the number of hospitals in China is huge, but more than 75% are unstructured and cannot play the value of “big dataâ€. On the other hand, whether it is modeling or training machines, it is inseparable from the real clinical environment. Most medical artificial intelligence products in China lack clinical environment.
Fan Xiaolei explained: "The current large amount of data is not standardized, and it is greatly influenced by doctor qualifications and personal style. Secondly, the lack of data and superficialization, now HIS system big data only records the basic things. Molecular Biological testing, genetic testing data, out-of-hospital follow-up data, these critical data for research, have not been recorded in the hospital system. The lack of a large amount of information has led to many difficulties in the application of this part of the data. Although there are now natural language The technology of processing, but for this part of the data processing, the value of the investment above this is less powerful."
Li Yishi from Haoyue Capital put forward the standard of “good†data: “We believe that high-quality data first exists in high-quality hospitals. It should be the experience of clinical experts, combined with guidelines, evidence-based medicine, including us more and more complete. , richer dimensional data, and produce high-quality data for scientific research. In the past, clinical research, the accumulated data in it, the data level is relatively high."
Technology can catch up, but the change of ideas is an invisible barrier. Liang Yi, chief commercial officer of Ding Ding Pharmaceuticals, bluntly said: "We can roughly imagine the future. 5G technology and the Internet of Things will make everything interconnected. This will have an impact on the business model and production of the entire medical industry . The future of the Internet of Things. It will be a big change. For all industries, if you don't understand the Internet of Things, you don't understand soft things, and you only know that hard-to-produce such enterprises are going to be eliminated. Especially now the domestic backward pharmaceutical companies. ."
Liang Yi added: "When a new product is listed, the pharmaceutical company is still a traditional idea, that is, to communicate with the doctor, and then to support the whole upstream and downstream links of this product, the ecological environment of all aspects does not do any research and development. It only refers to the research and development of products, research and development is also the research and development of business models, and your marketing department also needs to do research and development. This has not been done once in the pharmaceutical industry for decades."
As a tool to save a lot of manpower and empower all aspects of the medical industry, artificial intelligence is reflected in various industries in the medical and health industry. Artificial intelligence can help solve the problem of shortage of doctors' resources in hospitals and provide patients with more accurate and efficient services. In the pharmaceutical company, AI wants to meet the research and development needs of pharmaceutical companies, and to solve the recruitment of accurate patients, the primary problem is to generate a large amount of data. In the future, artificial intelligence enterprises need to cooperate with big data companies or pharmaceutical companies that have already accumulated a large amount of data. To achieve accurate patient recruitment, specialized patient organizations are also needed.
Pharmaceutical companies have been unable to leave artificial intelligence, but how to pass the data is a priority.
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