Not long ago, GPT-4 has taken the lead in achieving commercial cooperation in the medical field. Nuance Communications, a subsidiary of Microsoft, released DAX, a voice-enabled medical record generation application integrated with OpenAI's GPT-4. In the early stage, Baidu Wenxin said that many Internet medical services, medical informatization, innovative medical equipment, medical insurance, etc. were connected for internal testing and tool development. Professionals expect that with the release of Baidu Qianfan's enterprise service model, it will further promote the development of medical AI. develop.
As a breakthrough innovation, ChatGPT opened the prelude to the rapid development of the large language model industry and the generative AI industry. The generalization of multimodal AI has become a future trend.
ChatGPT has a wide range of usage scenarios, and the ChatGPT model can be applied to various text processing tasks and natural language processing scenarios to provide people with more intelligent and efficient services.
ChatGPT uses knowledge graphs in the training process, but in the highly specialized medical field, the utilization rate of knowledge graphs is very low.
As the core of medical artificial intelligence, medical knowledge graph is essentially a semantic network that shows the relationship between medical entities, and can formally describe the things in medical entities and the relationship between them.
In general, the medical knowledge map is constructed by continuously expanding entities and relationships on the basis of artificially constructed professional knowledge through algorithms and expert review, including medical concepts such as diseases, symptoms, drugs, and operations, and various medical relationships.
How to better combine the medical knowledge map and the ChatGPT-like large model is a major problem at present.
At present, there are problems in the construction of medical knowledge graphs such as cross-language, complex structure, strong professionalism, extensive data sources, lack of unified standards and integration methods, uneven data quality, and unclear data privacy and security guarantees. It created a lot of problems and directly affected the application of knowledge graphs.
The construction of medical knowledge graph needs to combine technologies such as natural language processing, machine learning, data mining, knowledge base and database, and has high requirements for medical professionals.
In addition, building a large-scale guideline knowledge graph requires a large number of medical professionals to label medical data, which is very time-consuming and labor-intensive.
Jinglianwen Technology is the leading enterprise in the AI basic data industry. It has a large number of high-quality medical data reserves and 100G of relevant medical knowledge texts, covering the latest research results in different medical fields; it has a large number of professional medical papers from many sources at home and abroad. Search platform, cooperation resources of more than 40 professional universities and more than 40 domestic and foreign professional medical organization associations; with 100G high-resolution and accurate medical images, including various medical images, such as CT, MRI, ultrasound, etc., can Let AI learn and diagnose better. These data can allow AI to better understand and simulate scenarios such as doctor-patient communication and diagnosis and treatment processes, improving the accuracy and efficiency of AI diagnosis. All the data are labeled and checked by professional medical personnel to ensure the high quality of the data.
Jinglianwen Technology has rich medical expert resources, which can extract knowledge from data of different sources and structures, and form knowledge and store it in the knowledge map. Experts in the medical field can label data information in vertical fields in an all-round way to ensure data quality and meet current labeling needs.
Jinglianwen Technology has a team of 5,000 professional medical students with rich annotation experience, has reached in-depth cooperation with 10 professional medical schools, has rich experience in image and text annotation, and can provide image and NLP related data collection and data for large-scale medical treatment Labeling service, deploy relevant labelers to provide services according to customer needs.
JLW Intelligent Medical Labeling Platform supports multiple types of medical data labeling, which can provide enriched, precise and structured medical knowledge for AI medical large models, and provide a more scientific and accurate guarantee for medical data customized labeling services.
Jinglianwen Technology provides a one-stop AI data solution for the whole process from data collection, cleaning to labeling, covering multiple vertical fields, assisting artificial intelligence enterprises to solve the corresponding problems of data links in the entire artificial intelligence chain, and satisfying different application scenarios According to the needs of various training data, promote the application of artificial intelligence in more scenarios, and contribute value to the construction of a complete AI data ecology.
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