Question

Write an essay (3000-6000 words) on the tittle A Survey on "Artificial Intelligence in Healthcare"

Give an abstract, introduction and definitions

Give the Modern Trends Advancements, Advantages, Disadvantages, Open Issues, and Challenges.

Give an overlook on the future of Artificial Intelligence in Healthcare

Answer 1

ARTIFICIAL INTELLIGENCE IN HEALTHCARE

ABSTRACT

Artificial Intelligence (AI) healthcare system and, as a result, help reduce costs and improve outcomes for payers, providers, and patients.Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.There are already a number of research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks, such as diagnosing disease. Today, algorithms are already out performing radiologists at spotting malignant tumours, and guiding researchers in how to construct cohorts for costly clinical trials. However, for a variety of reasons, we believe that it will be many years before AI replaces humans for broad medical process domains.

INTRODUCTION

Artificial intelligence (AI) and related technologies are increasingly prevalent in business and society, and are beginning to be applied to healthcare. These technologies have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer and pharmaceutical organisations.We cannot afford this level of error rate in healthcare. That’s where AI comes in. With a second opinion, AI can reduce misdiagnosis by up to 85%,And it can give general physicians the level of accuracy of specialist dermatologists.

Artificial intelligence is defined as the combination of science and engineering on creating intelligent computer systems that can perform tasks without receiving any instructions directly from humans.

Medical artificial intelligence is primarily concerned with the construction of artificial intelligence programs that perform diagnosis and make therapy recommendations.It simplifies the lives of doctors, patients, and hospital administrators by performing tasks that are typically done by humans. AI has countless applications in healthcare.

Artificial intelligence is definitely a game changer in the field of healthcare. The first reason being that it can process data faster than human beings. AI can be used from non-intrusive diagnosis to assisting doctors on surgery. They can also be great nursing assistants and also offer guidance to patients for certain conditions.

AI technologies used in healthcare:

• Machine learning
• Machine vision
• Natural Language Processing
• Robotics

Role of AI in healthcare:

AI in healthcare, which will make life easier for doctors and nurses both. But at the end of the day, AI, for the foreseeable future, will be an excellent tool for speeding up tasks that doctors and nurses used to do completely manually. It will save time, money and lives. It just won’t give you robots in doctors’coats.

With the help of advanced analytics, artificial intelligence and machine learning some of the most exciting advances are coming about in healthcare. Advances in AI interfaces, personalized medicine, predictive healthcare and advances in diagnostics all come down to the application of machine learning to help patients have access to smarter healthcare.

Although computers and robots will probably never completely replace nurses and doctors, Artificial Intelligence and machine learning are improving outcomes, transforming the healthcare industry and changing the way doctors think about providing care. Machine learning is just beginning to scratch the surface of personalized care, predicting outcomes and improving diagnostics.

Replacing manual labor

To get an idea of how machine learning will change things, we can take a look at Arteries and how it uses machine learning. By mining a data set of more than 3,000 cardiac cases, Arteries was developed where it looked at blood flow and the heart. By being curved to an MRI machine, it uses MRI images to generate editable contours and Arteries to examine blood flow. The machine helps in providing an accurate picture of a heart in seconds which once took an hour.

Previously, manual labour was required more than creative thinking to create accurate pictures but with the help of artificial intelligence that hour of Manual Effort has been freed. Doctors have now come up with potential treatments instead of taking the time to piece together MRI images while they leave this sort of work to machine learning and artificial intelligence.

A better Diagnosis System

In order to diagnose a patient, think about the countless conditions and symptoms which a doctor needs to remember and recall at the drop of a hat. Machine learning and artificial intelligence are superior at memorizing massive amounts of data as compared to a human.

Artificial intelligence looks after multiple symptoms described by a human to quickly diagnose what is the potential cause of the symptoms and using machine learning, could learn which diseases are more likely, all the while, continuously improving its accuracy over time.

A team of doctors discovered that they could identify people with pancreatic cancer and used advanced machine learning to analyze search queries even before they received a diagnosis. The study focused on search queries which indicated that someone had been diagnosed with pancreatic cancer. They then worked backward to find out if the earlier queries could predict the diagnosis.

Although the study didn’t result in a practical application still there is the possibility that in the near future, systems would be set up to warn a user to go get tested if the search queries suggest a particular disease like pancreatic cancer.

Another important advancement is being made in matching the best potential foster families with children. The “Every Child A Priority” (ECAP) system makes use of a sophisticated matching algorithm which predicts the best match between a foster family and a child, reducing the no. of moves a child needs to make and improves the potential for permanent placement.

• Disease prediction
• Drug manufacturing
• Treatment decision
• Surgery
• Managing medical records and other data
• Virtual nurses
• Healthcare monitoring

ADVANTAGES

1. Early Detection: AI is used to detect diseases like cancer and more accurately and in their primary stage. This early detection of diseases helps to better patient care and understanding of the disease stage as well as cure.
2. Robot-Assisted Surgery Process: Cognitive surgical robots can use information from different surgical encounters to enhance surgical techniques. Several medical teams can get insights into useful data from pre-operative medical records. This technique is useful to reduce the scope of error and time of treatment.
3. Automated Image Diagnosis: Over the last years, AI has helped in the progress of medical imaging. Many times, the storage of medical images is creating a problem. In addition, different complexities of conducting analysis and deciphering images have led to more efficient applications.
4. Dosage Error Reduction: It is very important to prescribe the dosage of the patient accurately, or else there might be penalties to pay. Thus, it helps to decrease the margin of medical errors that may occur while giving medicines to patients.
5. Drug Creation: Drug creation and discovery is one of the most trending applications for AI in healthcare. Generating different avenues of pharmaceuticals by using clinical trials can use excessive money and time. However, applications of AI can make the procedure cheaper and faster. By scanning the medicines, the application can redesign them and fight the disease.

• Leading to advancements in healthcare treatments
• The ability to quickly and more accurately identify signs of disease
• Patients can ask medical questions and receive answers in the absence of a doctor
• Reduces the treatment cost
• Makes the treatment decision faster
• Helps to reduce human errors
• Reading volumes of data and texts in seconds

​​​​​​​CHALLENGES

• Managing and Integrating Large Data Sets

Machine learning has no utility unless it is fed data-sets from which to learn. In the healthcare field, the availability of data is not an issue. Thanks to the HI TECH component of the American Recovery and Reinvestment Act (ARRA), healthcare data is now readily available both in structured and unstructured formats.

• Interoperability

Related to the issue of integrating large data sets is interoperability. To reap the full benefits of machine learning, it's crucial to synthesize medical and patient data into one system that is accessible to all providers in real-time.

One solution is to migrate all healthcare data into one system that can meet the needs of providers, payers, and patients. However, given the complexity of the healthcare system and its many moving parts, this can be a tall order. A more practical approach would be creating interoperability between the existing systems.

Interoperability will allow the various systems to "speak" with each other and enable providers to keep using their current systems. Providers can use a mix of different systems to manage different functions, as suit their needs, while allowing medical and patient data to be shared with others within the healthcare system. This effectively creates a single database that machine learning can work on in order to provide predictive analytics that can help patient care and improve health outcomes.

• Protecting Data Security and Patient Privacy

The collection of large data-sets, particularly those concerning an individual's medical history, necessarily raises the question of privacy. Currently, there are legal limitations on access to medical data. This restricted access is a barrier to developing robust health-focused algorithms.

The following could be the significant developments in healthcare:

• Managing Medical Records and Other Data
• Doing Repetitive Jobs
• Virtual Nurses
• Precision Medicine
• Health Monitoring
• Healthcare System Analysis
• Drug Creation

DISADVANTAGE

AI has been increasingly incorporated throughout the healthcare space. Machines can now provide mental health assistance via chatbot, monitor patient health, and even predict cardiac arrest, seizures, or sepsis. AI can offer diagnoses and treatments, issue reminders for medication, create precise analytics for pathology images, and predict overall health based on electronic health records and personal history — all while easing some of the burden placed on doctors​​​​​​​.

EXAMPLE

AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data.

FUTURE

It is one of the most largest growing industries in the world right now. The idea of bringing healthcare services right on your smartphone has helped in making people more aware about their health.However, the intuition and sixth sense of a cancer expert will continue to hold the upper hand, and a group of such cancer experts will be the best recommendation that anyone can expect for several years (if not decades) to come​​​​​​​.Until we allow computers to vote I'm not that sanguine about AI improving our healthcare delivery system. On the other hand I am hopeful that AI will eventually be able to help us physicians make certain decisions better and with less effort.Future of Healthcare is evolving rapidly with innovative technologies and AI is one among the tool of transforming Healthcare. Healthcare data is extremely complex and extremely crucial also because it involves the risk of life.​​​​​​​A positive impact if used judiciously. Its contribution in the field of Diagnosis, assessment tools and robotic surgeries is immeasurable.