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Health Care, AI

Technological Innovation, Artificial Intelligence (AI)

Artificial intelligence (AI) has emerged as a key driver and brings a huge impact in the paradigm of the 4th Industrial Revolution. AI is expected to boost productivity by creating a powerful collaboration in areas like healthcare, manufacturing, transportation, or energy industry and to help solve some of difficult social problems.
Particularly, the collaboration in healthcare is anticipated acceleration in innovations in medical services and eventually improvement on our quality of life.

Paradigm Shift in Medicine:One-size-fits-all Treatment to Personalized Medicine

Currently, with integration of AI technology, a paradigm shift in medicine occurs from one-size-fits-all treatment to early diagnosis, prevention, and personalized medicine.
While the conventional medicine paradigm was focused on universal and empirical treatment, the new paradigm in medicine drives disease prevention, early diagnosis, and customized treatment. AI accelerates to transform the practice of medicine with analysis and utilization of genomic data, health information, personal life style, etc. AI-based medicine is likely to facilitate in all medical services, such as Prediction·Prevention, Diagnosis, and Prescription·Treatment, resulting in a more precise and effective customized treatment depending on individual’s health and disease conditions.

AI innovations have a strong potential in healthcare. In Prediction·Prevention, AI allows individuals to prepare for disease before it develops based on analysis of genomic data, health information, personal life style, etc. AI technology at this process could be applied in prediction of various diseases; cancer, sepsis, heart disease, adult disease, etc.
Medtronic Inc., a medical device company, worked with IBM Watson Health teams to develop the Sugar.IQ personal diabetes assistant which is designed to manage the personalized daily diabetes with continuous analysis how an individual’s glucose levels respond to food, insulin intake, daily routines and other factors. UC Berkeley and University of Incheon published a journal on predicting inherited susceptibility to cancer by machine-learning method and researchers from Edinburgh University and the Institute of Cancer Research, London, have used AI to predict how cancers will progress and evolve by identifying the patterns in DNA mutation within tumors.

Sugar. IQ, a digital diabetes assistant,
Developed by Medtronic

Additionally, AI delivers a personalized prescription and treatment as it enhances the accuracy in disease diagnostics. AI-based application with a deep learning in medical image analysis brings a beneficial result as making pathological assessment.
South Korea-based startup VUNO developed AI-based software for bone age assessment and for neurodegenerative disorders. Lunit, a South Korea-based company, and Google developed an AI-based analysis software using deep-learning for Chest x-ray to diagnose lung cancer. GlaxoSmithKline PLC (GSK) established a collaboration with the University of California to accelerate the new drug discovery using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR). This collaboration will build on GSK’s existing collaboration with 23andMe.
Using AI and machine learning, correlations between genetic variants and disease can be identified and lead to selection of patients who have beneficial effects to drug for clinical trials, ultimately resulting in accelerated the drug discovery.

In addition to accuracy enhancement in disease prognosis and prediction using AI systems, appropriate prescription and treatment plans for each patient would be implemented. Watson for Oncology developed by IBM recommends different treatments to cancer patients depending on cancer progression and Google also developed an AI-based system to predict the medical outcomes. Trials for new drug applying AI systems will bring a significant impact on cost savings and time-consuming. Through deep learning and big data analysis, InSilico Medicine developed AI system that enables to identify the potential drug candidates for cancer, Parkinson's Disease, Alzheimer's Disease, etc.