Big data in healthcare is studied because electronic health data sets are large, complex and growing. They contain 90% unstructured data that will increase 25 times over the next decade. Examples of artificial intelligence in healthcare include IBM Watson which provides evidence-based treatment options to oncologists, Medical Sieve which assists with clinical decision making in radiology and cardiology, and an app from AiCure supported by NIH that uses a smartphone's camera to confirm patients are adhering to their prescriptions. Deep Genomics also aims to identify patterns in genetic data to inform doctors about the effects of genetic variations at a cellular level. Overall, big data and AI can help make the right healthcare decisions for patients.

2.  Why we study Big Data in healthcare system?
 Big Data Sources and technique
 Some Examples of Artificial Intelligence in Healthcare

3.  Electronic health data sets are large,
continuously growing, complex, difficult to
manage with traditional software
 90% Unstructured Data
 25 X as much data over coming
decade(One Exabyte by 2020) -Kaiser

8.  Designing treatment plans:
IBM Watson launched program for oncologists
Provides evidence-based treatment options
 Assisting repetitive jobs:
 Medical Sieve : Next generation “cognitive assistant”
with analytical, reasoning capabilities and wide range
of clinical knowledge.
Assist clinical decision making in radiology and
cardiology.

9.  Health assistance and medication management:
AiCure App supported by The National Institutes of
Health is a HIPAA-compliant software
 Use smartphone’s webcam and AI to autonomously
confirm that patients are adhering to their
prescriptions
Precision medicine:
Deep Genomics aims at identifying patterns in huge
data sets of genetic information and medical record
Tell doctors what will happen within a cell when DNA
is altered by genetic variation

10.  Stephen Hawking said that development of full
artificial intelligence could spell the end of the
human race
Unraveling Big Data : make right decision at right
time for right patients