Abstract: An artificial intelligence medical examination and medical record generation system includes a medical examination database storing a medical examination item and a past medical examination result, a medical examination generation unit generating or updating a medical examination scenario by selecting a medical examination item through a medical examination-generation artificial intelligence based on patient data, a medical examination provision unit providing the medical examination scenario with an expression familiar to a patient through natural language processing of a medical examination-provision artificial intelligence based on the patient data, a medical examination analysis unit analyzing a patient answer to the medical examination scenario through structuralizing, standardizing, and encoding processes, a record generation unit generating a medical record familiar to medical personnel based on an analysis result generated by the medical examination analysis unit, and a transmission unit transmitting

Abstract: A system includes a database storing a questionnaire scenario, a questionnaire analysis unit analyzing a questionnaire answer from a patient by using an artificial intelligence, a primary diagnosis unit generating a primary diagnosis result by diagnosing the patient based on an analysis result, a medical device linkage unit selecting a necessary test item based on the primary diagnosis result and linked with a medical device, a multi-access edge computing (MEC) linkage unit processing the MEC in linkage with a mobile communication base station, a secondary diagnosis unit generating a secondary diagnosis result by using a test result received from the medical device linkage unit and/or the MEC linkage unit, an electronic medical record (EMR) linkage unit receiving EMR data in linkage with an EMR system, and a diagnostic performance enhancement unit enhancing performance of the questionnaire scenario by using the secondary diagnosis result and the EMR data.

Abstract: A diagnostic disease prediction system according to an embodiment is based on a remote interactive questionnaire to provide the patient and the medical personnel with a disease prediction result, thereby helping a medical consumer correctly identify his/her condition and select an appropriate medical department and helping medical personnel quickly select a patient in emergency. The system includes a database (DB) storing a questionnaire scenario, a questionnaire analysis unit analyzing a questionnaire answer acquired from a patient by using an analysis artificial intelligence, a disease prediction unit generating a disease prediction result by analyzing a patient symptom through a questionnaire result systematized using a diagnosis artificial intelligence, a test selection unit selecting a necessary test item based on the disease prediction result, and a communication unit delivering the disease prediction result and the selected necessary test item to the patient or a medical institution.

Abstract: A method for providing basis data for diagnosis is disclosed.

Abstract: A method for generating a discomfort graph is disclosed. According to one aspect of the present invention, disclosed is a method for generating a discomfort graph including receiving information on a discomfort start point from a user; dividing a discomfort period from the discomfort start point to a present point into a plurality of discomfort sections; receiving from the user discomfort information obtained by quantifying discomfort levels for the respective discomfort sections; and generating a discomfort graph showing a change in the user's discomfort during the discomfort period based on the discomfort information.

Abstract: A method for predicting a developmental disease is disclosed. According to one aspect of the present invention, disclosed is a method for predicting a developmental disease including preparing a list of multiple symptoms and a list of multiple diseases, respectively, matching the symptoms with the diseases related to the symptoms, differentially setting correlation scores for the symptoms matched with the diseases according to prevalence rates of the diseases for the symptoms, for each disease, providing a user with the list of multiple symptoms and receiving, from the user, at least one selection of the symptoms that correspond to the user, and deriving predicted disease information in an order of a highest sum score by summing up scores related to the symptoms selected by the user for each disease and calculating the sum score.