Artificial Intelligence is getting increasingly sophisticated at doing what humans do, but more efficiently, more quickly and at a lower cost. The potential for artificial intelligence in healthcare is vast, and are increasingly a part of our healthcare eco-system. When many of us hear the term “artificial intelligence”, we imagine robots doing our jobs, rendering people obsolete, and since artificial intelligence-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. Artificial intelligence in healthcare 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.
Artificial intelligence in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Artificial intelligence is the ability of computer algorithms to approximate conclusions without direct human input. What distinguishes artificial intelligence technology from traditional technologies in health care is the ability to gain information, process it, and give a well-defined output to the end-user. Artificial intelligence does this through machine learning algorithms and deep learning, these algorithms can recognize patterns in behavior and create their own logic. In order to reduce the error margin, artificial intelligence algorithms need to be tested repeatedly. Artificial intelligence algorithms behave differently from humans in two ways: (1) algorithms are literal: if you set a goal, the algorithm can’t adjust itself and only understand what it has been told explicitly; (2) and some deep learning algorithms are black boxes, the algorithms can predict extremely precise, but not the cause or the why. The primary aim of health-related artificial intelligence applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. Large technology companies such as IBM and Google, have developed artificial intelligence algorithms for healthcare. Additionally, hospitals are looking to artificial intelligence software to support operational initiatives that increase cost-saving, improve patient satisfaction, and satisfy their staffing and workforce needs. Companies are developing predictive analytics solutions that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing the length of stay, and optimizing staffing levels.
Artificial intelligence simplifies the lives of patients, doctors, and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost. One of the world’s highest-growth industries, the Artificial Intelligence sector was valued at about $600 million in 2014 and is projected to reach $150 billion by 2026. Undoubtedly, there is no other industry that artificial intelligence has touched so heavily as the healthcare industry. It all comes down to letting the medical practitioners do best what they are good at creativity, and let the machines do what they do great: precision and routine tasks as this will create a win-win situation for everyone involved. According to Accenture analysis, when combined, key clinical health artificial intelligence applications can potentially create $150 billion in annual savings for the American healthcare economy by 2026. Artificial intelligence has countless applications in healthcare, whether it’s being used to discover links between genetic codes, to power surgical robots, or even to maximize hospital efficiency; artificial intelligence has been a boon to the healthcare industry. One in three misdiagnoses results in serious injury or death. An estimated 40,000 to 80,000 deaths occur each year in the American hospitals related to misdiagnosis, and an estimated 12 million Americans suffer a diagnostic error each year in a primary care setting — 33% of which result in serious or permanent damage or death. In light of that, the promise of improving the diagnostic process is one of the artificial intelligence’s most exciting healthcare applications. Incomplete medical histories and large caseloads can lead to deadly human errors, immune to those variables, artificial intelligence can predict and diagnose the disease at a faster rate than most medical professionals. Let’s understand how artificial intelligence is changing the healthcare industry.
Developing new medicines with Artificial Intelligence
The drug development industry is bogged down by skyrocketing development costs and research that takes thousands of human hours. It costs about $2.6 billion to put each drug through clinical trials, and only 10% of those drugs are successfully brought to market. Due to breakthroughs in technology, biopharmaceutical companies are quickly taking notice of the efficiency, accuracy, and knowledge that artificial intelligence can provide. One of the biggest artificial intelligence breakthroughs in drug development came in 2007 when researchers tasked a robot named Adam with researching functions of yeast. Adam scoured billions of data points in public databases to hypothesize about the functions of 19 genes within yeast, predicting 9 new and accurate hypotheses. Adam’s robot friend, Eve, discovered that triclosan, a common ingredient in toothpaste, can combat malaria-based parasites.
Streamlining Patient Experience with Artificial Intelligence
In the healthcare industry, time is money. Efficiently providing a seamless patient experience allows hospitals, clinics, and physicians to treat more patients on a daily basis. American hospitals saw more than 35 million patients in 2016, each with different ailments, insurance coverage, and conditions that factor into providing service. A 2016 study of 35,000 physician reviews revealed 96% of patient complaints are about lack of customer service, confusion over paperwork, and negative front desk experiences. New innovations in artificial intelligence healthcare technology are streamlining the patient experience, helping hospital staff process millions, if not billions of data points, faster and more efficiently.
Mining and Managing Medical Data with Artificial Intelligence
Healthcare is widely considered one of the next big data frontiers to tame. Highly valuable information can sometimes get lost among the forest of trillions of data points, losing the industry around $100 billion a year. Additionally, the inability to connect important data points is slowing the development of new drugs, preventative medicine, and proper diagnosis. Many in healthcare are turning to artificial intelligence as a way to stop the data hemorrhaging. The technology breaks down data silos and connects in minutes information that used to take years to process.
Artificial Intelligence Robot-Assisted Surgery
Popularity in robot-assisted surgery is skyrocketing. Hospitals are using robots to help with everything from minimally-invasive procedures to open-heart surgery. Robots help doctors perform complex procedures with precision, flexibility, and control that go beyond human capabilities. Robots equipped with cameras, mechanical arms, and surgical instruments augment the experience, skill, and knowledge of doctors to create a new kind of surgery. Surgeons control the mechanical arms while seated at a computer console while the robot gives the doctor a three-dimensional, magnified view of the surgical site that surgeons could not get from relying on their eyes alone. The surgeon then leads other team members who work closely with the robot through the entire operation. Robot-assisted surgeries have led to fewer surgery-related complications, less pain, and quicker recovery time.
Concluding, the best opportunities for artificial intelligence in healthcare over the next few years are hybrid models, where clinicians are supported in diagnosis, treatment planning, and identifying risk factors, but retain ultimate responsibility for the patient’s care. This will result in faster adoption by healthcare providers by mitigating perceived risk and start to deliver measurable improvements in patient outcomes and operational efficiency at scale. Artificial intelligence in healthcare is already changing the patient experience, how clinicians practice medicine, and how the pharmaceutical industry operates, it is just the beginning.
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