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 raised breath rate.
According to researchers from University of Ontario Institute of Technology, premature infants who are about to be subjected to sepsis displayed the reduction of heart rate variability.
In terms of intensive care patients, TREWScore (Targeted Real-Time Early Warning Score) was developed with EHR big data and this allowed predicting sepsis shock about 28.2 hours earlier.

Additionally, Medtronic and IBM Watson displayed an AI, Sugar IQ, which allowed diabetic patients to constantly manage and predict their blood sugar level.
Sugar IQ suggests insight to control blood sugar level after when it analyze every monitored blood sugar and insulin level variance.
It makes patients to improve their good habit by providing proper information for individuals after when all of informative patterns are identified.

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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.
As AI can display risk factors of disease without any prejudice or preoccupation, it allows more accurate results than previous standard guideline.
Researchers from Nottingham University, England, identified that analyzed data via artificial intelligence included ethnicity, mental disorder, and oral corticosteroid as a critical risk factor while previous model included diabetes.

When AI, big data, and deep learning are combined together, it will more accurately display result rather than human as people are having limitation of concisely analyzing data.

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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 in our presented society.
Since AI is still developing on broad area and playing critical roles much faster and clearer than human, unemployment is becoming a serious issue.
Furthermore, deep learning has been developed since 2010 by using artificial neural network. It is similar to human brain as several layers and different algorithms can process learning in depth.
As an amount of high quality data has been increased and accumulated in medical field, deep learning became compatible with medical field.

Artificial intelligence can be classified to three types (narrow, general, super) and all of these are focused on solving problem which humans are having difficulty.
When general AI is compared to narrow AI, general AI can think and analyze information or data like human can do.
Moreover, super AI is not clearly developed yet but it will become available to develop itself even more than human can develop with their brain. This is the reason why super AI is controversial in our society.
In our presented medical society, professional physicians and researchers are more focused on developing pattern recognition, memorizing, and classifying knowledge of AI.
Best way for us to develop AI is combining with human ability and deriving new profit. If people can regulate and apply rules to AI in medical field, AI application can effectively work. 

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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 not being presented in EHR, limitations are still remaining in usage of deep learning process.
To solve this problem, professional physician needed to select critical cases from whole EHR database to increase specificity of prediction model until deep learning process developed.
As deep learning learned the whole data of EHR and identified the important source by itself, it allowed physicians to predict patient’s health status while processing or after medical treatment and presented the diagnostic name when patients discharged from hospital.
Deep learning was able to recognize the important source of whole EHR data, and this led researchers to find out that AI regarded impatient mortality as the first critical factor.
It also “scale up” the prediction model as deep learning could emit every process such as preparing and adding supplemental academic data.

An AI technology which Google has been presented is very efficient to predict and prevent patient’s risk as deep learning can analyze complex medical data and provide prospective results.

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