KR-20260064386-A - METHOD, APPARATUS AND SYSTEM OF PROVIDING CUSTOMIZED MEDICAL EDUCATION SERVICE FOR STOMACH CANCER BASED ON ARTIFICIAL INTELLIGENCE

Abstract

A method, apparatus, and system for providing an AI-based customized medical education service regarding gastric cancer are disclosed. An AI-based customized medical education service providing apparatus regarding gastric cancer according to one embodiment of the present disclosure comprises: a memory; and a processor that performs data communication with the memory and forms and provides a platform for providing the customized medical education service regarding gastric cancer to a terminal of a medical education subject. The processor may include: a communication interface module; a data collection and preprocessing module that collects medical information regarding gastric cancer education through the communication interface module and preprocesses the collected medical information for an AI learning module; an AI learning module that learns through an AI learning model based on the preprocessed medical information; and an education content generation module that generates medical education content regarding gastric cancer customized for the medical education subject based on the AI learning model learned according to the level and needs of the medical education subject.

Inventors

• 원창덕
• 이하예민

Assignees

• 가톨릭대학교 산학협력단

Dates

Publication Date

20260507

Application Date

20241031

Claims (20)

1. In an artificial intelligence-based device for providing customized medical education services regarding gastric cancer, Memory; and A processor comprising a platform that performs data communication with the above memory and provides a customized medical education service regarding gastric cancer to a terminal of a medical education subject, wherein The above processor is, Communication interface module; A data collection and preprocessing module that collects medical information regarding gastric cancer education through the above communication interface module and preprocesses the collected medical information for an artificial intelligence learning module; An artificial intelligence learning module that learns through an artificial intelligence learning model based on the above-mentioned preprocessed medical information; and A training content generation module comprising generating gastric cancer medical education content customized for the medical education subject based on the artificial intelligence learning model according to the learning level of the medical education subject, device.
2. In claim 1, The above processor is, Controlling customized gastric cancer medical education content generated by the above education content generation module to be output to the terminal of the medical education subject through the above platform, device.
3. In claim 2, The above data collection and preprocessing module is, Classifying and labeling images regarding gastric cancer education included in the medical information collected above, device.
4. In claim 3, The above processor is, Obtaining the platform access information of the medical education subject mentioned above, and Determining the previous medical education service usage history of the medical education subject based on the above platform access information, device.
5. In claim 4, The above processor is, If, as a result of determining the previous medical education service usage history of the medical education subject mentioned above, there is a usage history, the system controls the output of test data with a learning level higher than the learning level of the previous usage history from the medical education subject's terminal via the platform mentioned above. device.
6. In claim 5, The above processor is, Acquiring feedback data for test data of a higher learning level than the learning level of the previous usage history output from the terminal of the medical education subject through the above platform, and determining whether to upgrade the learning level of the medical education subject based on the acquired feedback data, device.
7. In claim 6, The above processor is, If it is determined that the learning level of the medical education subject has been upgraded based on the above feedback data, customized medical education content according to the upgraded learning level is generated and controlled to be output to the medical education subject's terminal through the platform. device.
8. In claim 6, The above processor is, If it is determined that the learning level of the medical education subject is not upgraded based on the above feedback data, the previously used medical education content of the medical education subject is called and controlled to be output to the medical education subject's terminal through the platform, Controlling so that a part corresponding to the content determined when upgrading the learning level in the previously used medical education content is provided first on the terminal of the medical education subject. device.
9. In claim 1, The above processor is, Map and save customized medical education content for each learning level, and When the medical education subject accesses the platform, a problem for a user learning level test is called and controlled to be output on the medical education subject's terminal, and Obtain response data for the problem for the learning level test of the medical education subject, and determine the learning level of the medical education subject according to the response data. By generating customized medical education content that matches the learning level of the medical education target determined above, Controlling to output to the terminal of the medical education subject through the above platform, device.
10. In claim 9, The above processor is, If, based on the above response data, there is no learning level that matches the previously stored learning level of the medical education subject, Set a learning level range including the aforementioned multiple previously stored learning levels, and By combining medical education content mapped to multiple learning levels included in the learning level range set above, new medical education content is created, Controlling to output to the terminal of the medical education subject through the above platform, device.
11. In a method for providing an artificial intelligence-based personalized medical education service regarding gastric cancer, performed by a processor of a device, Steps for collecting medical information regarding gastric cancer education; A step of preprocessing collected medical information for an artificial intelligence learning module; A step of learning through an artificial intelligence learning model based on the above-mentioned preprocessed medical information; A method comprising the step of generating gastric cancer medical education content customized for the medical education subject based on the artificial intelligence learning model according to the learning level of the medical education subject. method.
12. In claim 11, The method further comprises the step of controlling the generated customized gastric cancer medical education content to be output to the terminal of the medical education subject through the platform. method.
13. In claim 12, The above preprocessing step is, Characterized by classifying and labeling images related to gastric cancer education included in the medical information collected above, method.
14. In claim 13, A step of obtaining the platform access information of the medical education subject; and The method further comprises the step of determining the previous medical education service usage history of the medical education subject based on the platform access information. method.
15. In claim 14, The step of determining the previous medical education service usage history of the medical education subject mentioned above is, If, as a result of determining the medical education subject's previous medical education service usage history, there is a usage history, the method comprises the step of controlling the output of test data with a learning level higher than the learning level of the previous usage history from the medical education subject's terminal via the platform. method.
16. In claim 15, A step of obtaining feedback data for test data of a higher learning level than the learning level of the previous usage history output from the terminal of the medical education subject through the above platform; and The method further comprises a step of determining whether the learning level of the medical education subject is upgraded based on the feedback data obtained above. method.
17. In claim 16, The step of determining whether the learning level of the medical education subject mentioned above has been upgraded is: When it is determined that the learning level of a medical education subject has been upgraded based on the feedback data above, the system is characterized by generating customized medical education content according to the upgraded learning level and controlling it to be output to the medical education subject's terminal through the platform. method.
18. In claim 16, The step of determining whether the learning level of the medical education subject mentioned above has been upgraded is: If it is determined that the learning level of the medical education subject is not upgraded based on the above feedback data, the previously used medical education content of the medical education subject is called and controlled to be output to the medical education subject's terminal through the platform, Characterized by controlling that a part corresponding to the content determined when upgrading the learning level in the previously used medical education content is prioritized and provided on the terminal of the medical education subject. method.
19. In claim 11, A step of mapping and saving customized medical education content for each learning level; When the medical education subject accesses the platform, a step of controlling the retrieval of a user learning level test problem to be output on the medical education subject's terminal; A step of obtaining response data for the problem for the learning level test of the medical education subject; A step of determining the learning level of the medical education subject based on the above response data; A step of generating customized medical education content that matches the learning level of the medical education subject determined above; and The method further comprises the step of controlling output to be displayed on the terminal of the medical education subject through the above platform. method.
20. In claim 19, The step of determining the learning level of the medical education subject based on the above response data is, If, based on the above response data, there is no learning level corresponding to the previously stored learning level of the medical education subject, a step of setting a learning level range including the previously stored multiple learning levels; A step of generating new medical education content by combining medical education content mapped to a plurality of learning levels included in the learning level range set above; and The method comprises a step of controlling output to be displayed on the terminal of the medical education subject through the above platform. method.

Description

Method, apparatus and system of providing customized medical education service for stomach cancer based on artificial intelligence The present disclosure relates to medical education services, and more specifically, to a method, apparatus, and system for providing personalized medical education services to a subject regarding various types of cancer through artificial intelligence (AI) technology. There are various types of cancer. Furthermore, despite its dangers, cancer has now become a common disease. In fact, cancer is one of the top three causes of death in Korea, and despite advancements in medical technology, it has yet to be completely eradicated. For this reason, the necessity of cancer education for medical students and other subjects of medical education is undeniable. However, the current medical education system is conducted in a uniform and standardized manner using identical medical education content based on a pre-prepared curriculum, regardless of the level or needs of the subjects, which can be seen as reducing the efficiency of medical education. For example, conventional medical education has primarily relied on textbooks, lectures, and practical training; however, this educational method has limitations, such as the difficulty in inducing active participation from medical students and a significant disconnect from the actual clinical environment. In particular, for diseases with diverse forms and patterns, such as stomach cancer, the lack of image-based learning materials may reduce the effectiveness of learning. FIG. 1 is a schematic diagram of a customized medical education service system according to one embodiment of the present disclosure. Figure 2 is a configuration block diagram of the computing device of Figure 1. FIGS. 3 to 7 are flowcharts of a method for providing a customized medical education service based on artificial intelligence technology according to an embodiment of the present disclosure. FIGS. 8 and 9 are drawings illustrating an example of a user interface (UI) screen configuration provided through a platform according to an embodiment of the present disclosure. Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. The terms 'part, module, component, block' as used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of 'parts, modules, components, blocks' may be implemented as a single component, or a single 'part, module, component, block' may include a plurality of components. Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are directly connected but also cases where they are indirectly connected, and indirect connections include connections made via a wireless communication network. Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components. Terms such as "first," "second," etc., are used to distinguish one component from another, and the components are not limited by the aforementioned terms. Singular expressions include plural expressions unless there is an obvious exception in the context. In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context. The operating principles and embodiments of the present disclosure will be described below with reference to the attached drawings. In this specification, the term "device according to the present disclosure" includes all various devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may be in the form of any one of these. Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser. The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, a web server, etc. The above portable terminal may include, for example, all types of handheld-based wireless communication devi