One of the fastest growing sectors of the economy over the past decade has been healthcare, and in light of the growing threats from pandemics such as the coronavirus outbreak, the sector is about to expand again. To stay ahead of the demand for healthcare services and solutions, organizations around the world are turning to advanced techniques such as AI, machine learningAnd Big Data.
AI is going to play a huge role in healthcare. According to Acumen Research and Consulting, the global market will reach $8 billion by 2026 and there is a huge overlap of skills in AI and big data— where information processing is optimized to help solve business and real-world problems. AI and Big Data provide many potential benefits for individuals and businesses, including:
- Strengthening patient self-service with chatbots
- Diagnose patients with faster computer-aided design
- Analysis of image data to examine molecular structure in drug discovery, and by radiologists to analyze and diagnose patients
- Personalize treatments with more relevant clinical data
Let’s take a look at some examples of AI and big data at work in the health care sector.
How AI can predict heart attacks
Plaque is made up of substances that circulate in the blood, including cholesterol and fats. Over time, plaque builds up in the arteries, causing them to narrow and stiffen. Just as sink drains can become clogged with food and debris, arteries can become clogged with plaque, restricting blood flow and leading to a heart attack or stroke.
A medical test called coronary computed tomography angiography (CTA) takes 3D images of the heart and arteries. Plaque in the arteries is visible on CTA images, but measuring the amount of plaque can take an expert 25 to 30 minutes. SO researchers at Cedars Sinai developed an AI algorithm that allows a computer to perform the same task in just a few seconds.
The researchers fed a computer with 900 coronary CTA images already analyzed by experts. In this way, the computer “learned” to identify and quantify dental plaque in the images. The AI algorithm’s measurements accurately predicted the incidence of heart attacks within five years in 1,611 people who participated in a related research trial.
AI in preventive healthcare
The potential applications of AI in preventive healthcare are broad and profound. Beyond heart attacks, researchers are actively exploring the use of AI to predict a host of other diseases. For example:
Additionally, AI is already used in emergency rooms and intensive care units to help clinicians treat some of the most vulnerable and at-risk patients. A massive amount of data resides in electronic medical records, laboratory results, vital sign records, and medication logs. AI algorithms can help doctors and nurses identify patterns in data that alert them to a change in a patient’s condition or a risk of developing a serious complication. For example, AI can help:
- Early identification of sepsis, a life-threatening illness that occurs when the body’s immune system reacts in an extreme way to an infection.
- Identifying Fetuses in Distress Using Data from Fetal Heart Monitors
- Alert clinicians when patients on mechanical ventilators need adjustment
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AI for monitoring the mobility of hospitalized patients
Clinical staff are busy people. Take for example intensive care unit (ICU) nurses, who often have multiple critically ill patients under their care. Limited mobility and cognition during long-term treatments can harm patients’ overall recovery. Monitoring their activity is vital. To improve the results, researchers from Stanford University and Intermountain LDS Hospital installed depth sensors equipped with ML algorithms in patient rooms to track their mobility. The technology accurately identified movements 87% of the time. Ultimately, researchers aim to provide ICU staff with notifications when patients are in trouble.
Clinical trials for drug development
One of the biggest challenges in drug development is conducting successful clinical trials. As it stands, it can take up to 15 years to bring a new drug – potentially life-saving – to market, according to a report. report published in Trends in Pharmacological Sciences. It can also cost between $1.5 billion and $2 billion. About half of that time is spent on clinical trials, many of which fail. With AI technology, however, researchers can identify the right patients to participate in experiments. Additionally, they can monitor their medical responses more efficiently and accurately, saving time and money along the way.
Quality of electronic health records (EHR)
Ask any healthcare professional what the bane of their existence is, and there is no doubt that cumbersome EHR systems will appear. Traditionally, clinicians wrote or manually entered observations and patient information, and no two did the same thing. Often, they did this after the patient’s visit, which risked human error. With AI and deep learning voice recognition technologyHowever, patient interactions, clinical diagnoses, and potential treatments can be augmented and documented more accurately and in near real time.
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Physical robots also use AI
Robots (of the physical type) are used in many types of businesses today, such as manufacturing and warehousing. But robots are also increasingly used in hospitals, and many are designed to take advantage of AI. THE National Center for Biotechnology Information (NCBI) reported that physical robots are increasingly collaborating with humans and can be trained to perform various tasks through AI logic. And it’s not just about delivering supplies to hospitals. Surgical robots can “provide ‘superpowers’ to surgeons, enhancing their ability to see and create precise, minimally invasive incisions, suture wounds, and more.” With AI driving their decision-making processes, robots can improve the speed and quality of a wide range of medical services.
Improve population health
Population health studies the patterns and conditions that affect the overall health of groups (unlike “public health,” which focuses on how society ensures that people are healthier). Big Data is an important part of this effort. A recent article in Integrated highlighted various companies leveraging Big Data to help healthcare organizations and researchers read trends aimed at improving health conditions.
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For example, a company called Linguamatics in Cambridge, Massachusetts, uses natural language processing to mine unstructured patient data to detect relevant lifestyle factors and predict which patients may be at higher risk of illness. Another Santa Clara, Calif., company called Hortonworks helps organize and integrate billions of records so pharmaceutical companies can do better research for clinical trials, increase safety levels and get their products to market faster.
How Big Data can fight cancer
Big Data technologies are also used in the fight against cancer. As reported in National Geographic, Big Data technologies can process clinical data to reveal hidden patterns that enable earlier diagnosis of cancer. The earlier it is detected, the better the chances of treating it. Big data technologies are experts in analyzing genome sequencing to identify cancer biomarkers, and can also reveal groups at particular risk for cancer and find otherwise unknown treatments. The most progressive companies are using Big Data techniques to accelerate their analyzes and create treatments more quickly and with more tangible results.
AI Challenges in Healthcare
The use of AI technology in healthcare is exciting, but is not without its challenges. AI algorithms rely on identifying patterns in large amounts of data. If the data is biased, inaccurate, unrepresentative of a patient population, or compromised in any way, the resulting conclusions will also be flawed. Additionally, even once new AI-based clinical tools have been fully approved, the process of getting them approved by the FDA, adopted by hospitals, and accepted by insurance companies can be lengthy.
AI-based healthcare initiatives must also consider ethical concerns surrounding the mining of patient data. While AI applications can be useful in predicting patient behavior (e.g., who is likely to miss appointments, skip exams, or refuse treatments), they must do so in a way that preserves patient privacy and medical information.
Watch the video below to understand the role of Big Data in various industries like weather forecasting, healthcare, media & entertainment, logistics, travel & tourism, etc.
Bottom line: Advanced AI skills are taking healthcare to new heights
Whether you’re looking to upskill your team in healthcare research, product development, or healthcare services, AI and Big Data are helping to shape your strategy. Train for AI engineers, machine learning expertsAnd big data engineers can make the difference when individuals are trying to find the right niche. Adding these skills will help prepare you or your staff for the rigors of a bold new world of global healthcare.
