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In terms of heart diseases, researchers from Asan hospital, Seoul represented AI which can concisely predict ventricular tachycardia before 1 hour from when it strokes. This increased the viability of patients from heart attack but required the human effort to set an artificial neural network which was based on specific feature. Furthermore, researchers from Vuno
In these days, breath, temperature, heartbeat rate, blood sugar level, and other human body signals can be analyzed by AI and consequently indicate or predict our health status and diseases. Sepsis, a common deceasing factor of patients, is the best example which can be accurately recognized by varied temperature, blood pressure drop, reaction reduction, and
Although medical data is complicated, it can be analyzed and display new insight through AI. This can predict future disease attack and decrease readmission rate or reduce medical expense. The guideline derived from ACC/AHA, is not a perfect model to predict disease as it does not include any individual’s life style, habits, or risk factors.
In our past medical society, some researchers expected AI will replace professional physicians as it is based on big data and derive more accurate results than human without any bias. Machine learning, which learns data via math equations and setting rules like a human, is commonly used in filtering spam mails, face and voice recognition

Medical AI in Google

Google displayed the AI technology which can predict individual patient’s medical treatment results by analyzing their electronic health record (EHR) through deep learning system. It was difficult to analyze EHR as several complicated data such as the quantity of patients, different type of diseases, prescriptions, and surgical operations are presented. Moreover, since some data is
Along with the study of evaluating diabetic retinopathy by applying deep learning to retinal fundus image, predicting cardiovascular risk factor also became focused in medical field. As identified retinal fundus images can display distinctive features of optic disc or blood vessels, correlation between heart diseases and retinal disease, it allowed to predict the cardiovascular risk
Before when deep learning was not displayed in ophthalmology, diabetic retinopathy was needed to be evaluated and prescribed through human effort. IDx-DR is using the algorithm which applied two development operating set (high specificity & high sensitivity) to identify diabetic retinopathy and to evaluate patient’s common status. Referable and non-referable diabetic retinopathies were evaluated by
01:54:25The most common trend in medical AI society is using deep learning technology which can analyze medical image data. Deep learning is the process of training neural network which allows an algorithm to program itself by learning from a large set of models that perform the desired behavior. It is an important source in our