In this year, large models and artificial intelligence have become hot topics in all walks of life. In terms of quantity, compared with foreign large models, as of October 2023, there are 254 large model manufacturers and universities in China with a scale of more than 1 billion parameters, which is definitely far ahead. These large models can be divided into two categories: general large models and industry large models. In the medical field, the application of large models has also received extensive attention and support. Due to the complexity and variability of the medical field, as well as how traditional medical knowledge copes with update lag and data storage, this has brought unprecedented challenges to the field with the assistance of large models and artificial intelligence.
1. Lag in updating medical knowledge and challenges in data storage
In the era of big data, medical knowledge is updated rapidly, and new research results, advanced methods and drugs are constantly emerging. However, traditional medical knowledge is often updated relatively slowly due to the lack of motivation for continuous learning and Xi, limited information dissemination channels, and lack of accumulation of practice and experience. This will have a multifaceted impact on the medical field, including, but not limited to, difficulty meeting clinical needs, lagging behind the pace of technological development, and leading to medical errors or accidents.
At the same time, data security and privacy issues are also a major challenge. Traditional data storage technology can no longer meet the needs of the medical field, and the development of distributed storage technology, artificial intelligence and large model technology provides new possibilities to solve these problems. However, these technologies also bring new challenges, such as storage costs, difficulty in data processing, and data security and privacy protection.
2. Coping strategies and suggestions
Faced with the pain points of heterogeneity and decentralized storage of medical data, this can lead to information silos and limit physicians' need for understanding of emerging diseases or rare cases. So how to solve the problem of decentralized storage of data and the lag in the update of medical knowledge, the following are a few suggestions.
Innovative data storage technology: Improve data processing capabilities through the development of distributed storage technology, the application of big data energy model technology, and the strengthening of data security and privacy protection measures.
Establish a medical knowledge base: improve the quality and efficiency of information by building an authoritative medical knowledge base platform, integrating domestic and foreign medical information resources, and strengthening the standardization and standardized management of medical information resources.
Strengthen policy guidance and support: Promote the innovation and development of the medical field by formulating relevant policies and regulations that are conducive to the updating of medical knowledge and data storage, strengthening investment, and encouraging social capital to enter the medical field.
3. Examples of application scenarios for large models in the medical vertical field
Auxiliary diagnosis and medical image analysis
Auxiliary diagnosis is one of the important application scenarios of large models in the medical vertical field. By analyzing a large number of medical images and data, the large model can automatically Xi learn and identify disease characteristics and provide accurate diagnostic results and recommendations for doctors. The methods and steps for using large models in medical verticals include data preprocessing, model training, model evaluation, and optimization.
Large models in the medical vertical field are used for the analysis and interpretation of medical images to help doctors quickly and accurately judge the condition. The methods and steps of using large models in medical vertical fields include image preprocessing, model training, and post-processing of results.
For example, Yunzhisheng has developed a large-scale clinical medical knowledge graph, released a self-developed "Shanhai" large model, and jointly developed an automatic generation system of electronic medical records in outpatient doctor-patient dialogue scenarios, realizing the functions of noise reduction, doctor-patient role differentiation, information summary and automatic generation of medical records in the complex environment of the clinic. Improve the efficiency of electronic medical record entry for doctors by more than 400%. Save more than 40% of the time spent on a single patientAt the same time, it has improved the efficiency of doctors' outpatient clinics by more than 66%. The case analysis and effect display show that the model can automatically learn and Xi and identify features and abnormal manifestations in medical images, and improve the accuracy and efficiency of diagnosis.
Disease** and prevention
The large model of the medical vertical can also be used to analyze the data of an individual's genome, life Xi habits, etc., to reduce the risk of a certain disease, so as to take corresponding preventive measures. Methods and steps for using large models in healthcare verticals include data collection, model training, risk assessment, and recommendations for preventive measures.
For example, Shukun (Beijing) Network Technology Co., Ltd. is a smart health management assistant smart health management assistant, which mainly serves hospitals (including district hospitals and primary medical institutions) and relevant departments of the National Health Commission, and provides digital support through services such as "regional digital doctor assistant", "digital cloud medical language model", "regional health digital supervisor", etc., forming a full closed loop of "screening-evaluation-diagnosis-treatment-management" of regional major diseases.
Among them, it provides residents with full life cycle smart health services, carries out 60,000 integral screenings in three years, and is expected to detect about 9,000 medium and high-risk groups, and in the long run, there will be opportunities to reduce the expenditure of at least one million yuan for a single family through health services, save more than one billion yuan for medical insurance, and further improve the efficiency of medical insurance. The model is able to accurately determine the risk of an individual developing a certain disease, providing strong support for prevention and prevention.
Personalized** program
The personalized plan is to provide the most suitable plan for the patient according to his specific situation and needs. The large model in the medical field is used to analyze the patient's condition, genome, historical cases and other data to provide patients with personalized solutions. The methods and steps of using large models in medical verticals include data collection, model training, and program recommendations. Automatically generate the most suitable personalized plan according to the specific situation of the patient to improve the effect and quality of life of the patient.
In the era of large models, traditional medical knowledge is facing great challenges. In order to meet these challenges, we need to constantly innovate and update traditional medical knowledge to meet the needs of the times. At the same time, we should make full use of modern scientific and technological means to improve data storage and processing capabilities, integrate medical information resources, and maximize resource sharing and utilization. In order to promote the continuous innovation and development of the medical field.