Question

Write a paper (3000-6000 words) on the topic " Revolutionizing Healthcare through Artificial Intelligence, Machine Learning and Big Data".
Give an abstract, introduction and definitions.
List and Explain the Different Types of Artificial Intelligence, Machine Learning, and Big Data.
Give the Modern Trends Advancements, Advantages, Disadvantages, Open Issues and Challenges.
Give an overlook of the future of Artificial Intelligence, Machine Learning, and Big Data.

Answer 1

Answer:-

The increase in machine learning use cases in healthcare worldwide is evidence enough that AI and deep machine learning are no longer the future of healthcare; they are the drivers of the modern day healthcare industry. The technology has seeped through most spheres of the healthcare ecosystem and other industries with ties to the medical world and plays a very crucial role. Amid concerns of whether it’s ethical or not, healthcare AI has flourished so much in the last five years, that the big boys of tech are scrambling to get a piece of the AI pie by investing billions of dollars.

Artificial intelligence and machine learning, in particular - is revolutionizing the modern day healthcare ecosystem by injecting transformative automation, accurate research, efficiency and acceleration of service delivery in the healthcare space. So, what exactly is this artificial intelligence and why is it so significant in the healthcare industry? What are some of the machine learning use cases in healthcare today?

Artificial intelligence is a simulation of human thinking and intelligence by a set of computer systems. For the systems to mimic human thinking, computer systems need to be ‘trained’ over a period of time through huge chunks of data and special algorithms. Deep machine learning is an application of AI which combines complex algorithms, the human neural network architecture and very large chunks of data fed to the systems to help them deliver consistently powerful results.

In healthcare, AI and machine learning to have found a home. The medical industry is very data-reliant and the ability of computer systems to quickly process the available medical data, provide accurate deductions and even predict trends from the provided data, could not come at a better time in the healthcare industry.

AI and deep machine learning are no longer the future of healthcare; they are the drivers of the modern day healthcare industry; numerous companies, research facilities, and even healthcare facilities have already incorporated AI and machine learning into their daily activities. Here are highlights of machine learning uses in healthcare.

Healthcare Machine Learning Use Cases

1. Drug Manufacturing and Development
   
   The drug manufacturing and development stage includes a rigorous testing phase to determine the success rate of that particular drug depending on several factors. This painstaking exercise requires a lot of time and takes up a lot of resources.
   
   To cut on the testing period and the budget required to develop new drugs for diabetes, The Clinical Machine Learning Group uses AI and deep machine algorithms. Project Hanover from Microsoft in conjunction with the Knight Cancer Institute are also using AI to create stronger and better cancer treatment options for patients. Cyclica gives useful insights into the use of several different pharmaceutical agents with an ability to curb multiple disease targets and pathways to develop drugs with no side effects to patients. These are just some of the hundreds of startups which are using AI and Machine Learning to create better drugs.
2. Clinical Trial of Developed Drugs
   
   Once developed, drugs need to be tested through clinical trial research. The clinical trial research entails picking of the best possible candidates who have the ‘optimal’ properties for a potential target. The candidates are then put under the drug to test its effectiveness and any side effects. The picking of the ‘perfect’ candidate for this exercise requires a lot of time researching, and AI is helping make this process quicker and more effective.
   
   A2A Pharmaceutical is helping drug researchers to pick the best candidates for clinical trial research for the treatment of numerous diseases. The startup focuses mainly on cancer and bacterial infections and uses AI to collect genetic data of the subjects, their response to the new drug and any biological signs of side effects of the drugs. Antiverse is equally involved with drug testing and helps researchers to pick the best candidates for antibodies in less than 24 hours.
3. Smart Management of Medical Records and Other Important Data
   
   Both AI and the medical industry, rely heavily on data and available records. Doctors and nurses need previous health records of patients, the history of diseases within one’s family, and other important information to be able to treat the patients. AI helps to process available health records and suggest the best possible treatments for patients with different diseases.
   
   Prognos, a startup in the US, has access to more than 5 billion medical records drawn from close to 100 million patients across the country and their data is used by healthcare facilities to improve decisions on administering drugs to patients. Google’s Cloud Vision API is also helping hospitals and other facilities to collect and digitize data through OCR and store the information for future reference.
4. Personalized Affordable Healthcare
   
   Machine learning is also helping make healthcare affordable to everyone, and one startup from the UK called Babylon is actualizing this universal dream.
   The company is looking for fresh funding to further expand its services which combine the computing power of computer systems with the best medical knowledge to personalize health services to people and make the services as affordable as possible.
   
   Sense.ly is also customizing patients’ experiences through their assistant called Molly that follows up patients’ treatments and monitors their conditions as well. National Institute of Health’s AiCure App is designed to monitor patients and ensure that they truly are taking their medication as prescribed through a webcam.
5. 5. Prediction of Disease Occurrences
   
   Machine learning can be used within the healthcare industry to predict the occurrence of certain diseases and health epidemics.
   By processing large amounts of data real-time data from social media and other sources, these systems can predict epidemics before they hit. AIME is a startup that uses artificial intelligence to analyze data and determine the location of potential outbreaks of diseases such as Dengue fever.

In conclusion, AI and Machine Learning are already heavily influencing the healthcare industry, and so far, we have only scratched the surface. Machine learning healthcare promises better, efficient and affordable universal services and its uptake will take the world by storm.

When many of us hear the term "artificial intelligence" (AI), we imagine robots doing our jobs, rendering people obsolete. And, since AI-driven computers are programmed to make decisions with little human intervention, some wonder if machines will soon make the difficult decisions we now entrust to our doctors.

According to David B. Agus, MD, a professor of medicine and engineering at the University of Southern California Keck School of Medicine and Viterbi School of Engineering, it's important to separate fact from science fiction, because AI is already here -- and it's fundamentally changing medicine.

Rather than robotics, AI in health care mainly refers to doctors and hospitals accessing vast data sets of potentially life-saving information. This includes treatment methods and their outcomes, survival rates, and speed of care gathered across millions of patients, geographical locations, and innumerable and sometimes interconnected health conditions. New computing power can detect and analyze large and small trends from the data and even make predictions through machine learning that's designed to identify potential health outcomes.

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Machine learning uses statistical techniques to give computer systems the ability to "learn" with incoming data and to identify patterns and make decisions with minimal human direction.

Armed with such targeted analytics, doctors may be better able to assess risk, make correct diagnoses, and offer patients more effective treatments, says Agus, the author of The Lucky Years: How to Thrive in the Brave New World of Health and The End of Illness. He believes AI's potential to improve health care is "staggering."

"We have lots of data that we've been collecting over decades," he says. "For the first time, computing power allows us to use the data in a way to benefit patients."

The challenge, he says, is that "an individual has hundreds of thousands of health care data points, if not millions. So when you have data sets of hundreds of thousands of patients, and each patient has a million data points, the data need to be collected appropriately and correctly for the power of machine learning" to bear fruit.

He offers an example. "A study came out recently that showed that if you have ovarian cancer, and you happen to also be on a beta-blocker -- a drug that [can be] used for blood pressure -- you lived four-and-a-half years longer," he says. "This is an observation we would never have come up with through biology. Big data shows us. Now [this finding] needs to go to a big trial to see if it's real."