Healthcare has been one of the fastest growing economic sectors over the past decade, and with the growing threat of pandemics such as the coronavirus outbreak, the healthcare industry is set to grow again. To stay ahead of the demand for healthcare services and solutions, organizations around the world are turning to advanced technologies such as AI, machine learning, and big data.
AI will play a big role in healthcare. According to Acumen Research and Consulting, the global market will reach $8 billion by 2026, and there is a lot of skill overlap between AI and big data, optimizing information processing to help solve business and real-world problems. AI and big data offer many potential benefits to both individuals and businesses, including:
Enhance patient self-service with chatbots Diagnose patients with faster computer-aided design Analyze image data to explore molecular structures in drug discovery, and for radiologists to analyze and diagnose patients Personalize treatment with more insightful clinical data
Let’s look at some examples of AI and big data being used in healthcare.
How AI can predict heart attacks
Plaque is made up of substances that circulate in your bloodstream, like cholesterol and fats. Over time, plaque can build up in your arteries, causing them to narrow and harden. Just like a sink drain can get clogged with food and debris, your arteries can also get clogged with plaque, restricting blood flow and potentially causing a heart attack or stroke.
A medical test called a coronary computed tomography angiogram (CTA) takes 3D images of the heart and arteries. Plaque in the arteries can be seen on a CTA image, but measuring the amount of plaque takes an expert 25-30 minutes. So researchers at Cedars-Sinai developed an AI algorithm that allows a computer to perform the same task in just seconds.
The researchers fed the computer 900 coronary CTA images that had already been analyzed by experts. The computer then “learned” how to identify and quantify plaque in the images. The AI algorithm’s measurements allowed it to accurately predict the five-year incidence of a heart attack among 1,611 people who participated in the associated research trial.
AI in preventive medicine
The potential applications of AI in preventive medicine are broad and deep. Beyond heart attacks, researchers are actively exploring using AI to predict a range of illnesses and diseases. For example:
Additionally, AI is already being used in emergency rooms and intensive care units to help doctors treat the most vulnerable and high-risk patients. Electronic medical records, lab results, vital signs records, and medication records contain vast amounts of data. AI algorithms can help doctors and nurses identify patterns in the data that could warn of a patient’s changing condition or risk of developing serious complications. For example, AI can help:
Early detection of sepsis (a life-threatening condition that occurs when the body’s immune system has an extreme response to an infection), identifying fetal distress using fetal heart rate monitor data, alerting clinicians when ventilated patients need adjustments, Master big data and Hadoop frameworks, leverage the capabilities of AWS services, and use database management tools with Big Data Engineer Training.
AI for ambulatory monitoring of hospitalized patients
Clinical staff are busy people. Intensive care unit (ICU) nurses, for example, are often responsible for multiple critically ill patients. Limited motor and cognitive abilities during prolonged treatment can negatively impact a patient’s overall recovery. Monitoring patient activity is essential. To improve outcomes, researchers from Stanford University and Intermountain LDS Hospital installed depth sensors powered by ML algorithms in patients’ rooms to track their movements. The technology accurately identified movements 87 percent of the time. Researchers ultimately aim to notify ICU staff when patients are in critical condition.
Clinical trials for drug development
One of the biggest challenges in drug development is successful clinical trials. According to a report published in Trends in Pharmacological Sciences, currently, it can take up to 15 years and cost $1.5 to $2 billion to bring a potentially life-saving new drug to market. About half of that time is spent on clinical trials, many of which fail. However, with AI technology, researchers can identify the right patients to participate in experiments. Additionally, patients’ medical responses can be monitored more efficiently and accurately, saving time and money.
Electronic Health Record (EHR) Quality
Ask any healthcare professional what their biggest pain points are and they’ll undoubtedly mention cumbersome electronic medical record systems. Traditionally, clinicians manually wrote down or entered observations and patient information in a way that varied from person to person. Often, they wrote or entered information after seeing the patient, introducing human error. However, speech recognition technology powered by AI and deep learning can augment and record patient interactions, clinical diagnoses, and potential treatments more accurately and in near real-time.
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Physical robots also use AI
Robots (physical ones) are used today in many different industries, including manufacturing and warehousing. But robots are also increasingly being used in hospitals, many of which are designed to leverage AI. The National Center for Biotechnology Information (NCBI) reported that physical robots can be trained to be more collaborative with humans and perform a variety of tasks enhanced by AI logic. And it’s not just about delivering supplies in hospitals. Surgical robots “give surgeons ‘superpowers,’ improving their ability to make precise, minimally invasive incisions, suture wounds, and more.” With AI driving the decision-making process, robots can improve the speed and quality of a wide range of medical services.
Improving population health
Population health studies patterns and conditions that affect the overall health of a population (different from “public health,” which focuses on how society can ensure more healthy people). Big data is a big part of this effort; a recent article from BuiltIn highlights various companies that are using big data to help healthcare organizations and researchers read trends and improve health outcomes.
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For example, a company called Linguamatics in Cambridge, Massachusetts, uses natural language processing to mine unstructured patient data to detect associated lifestyle factors and predict which patients are at higher risk for disease. Another company called Hortonworks in Santa Clara, California, helps pharmaceutical companies organize and consolidate billions of records so they can conduct better research for clinical trials, improve safety levels, and get products to market faster.
How big data can help fight cancer
Big data technology is also being used in the fight against cancer. According to a report by National Geographic, big data technology processes clinical data to uncover hidden patterns, leading to earlier cancer diagnosis. The earlier it is detected, the greater the chance of treatment. Big data technology is also adept at genome sequencing to identify cancer biomarkers, revealing groups at high risk for cancer and finding previously undiscovered treatments. The most progressive companies are using big data technology to speed up analysis and develop treatments that deliver faster, more specific results.
AI challenges in healthcare
The use of AI technologies in healthcare is exciting, but not without challenges. AI algorithms rely on identifying patterns from vast amounts of data. If the data is biased, inaccurate, not representative of the patient population, or compromised in any way, conclusions based on it will also be inaccurate. Additionally, even after a new AI-powered clinical tool has been fully validated, it can be a lengthy process to gain FDA approval, adoption by hospitals, and acceptance by insurance companies.
AI-powered healthcare efforts must also be mindful of ethical concerns about mining patient data: While AI applications can help predict patient behavior (likely to miss an appointment, skip a test, or refuse treatment), they must do so in a way that protects patient privacy and medical information.
Watch the video below to understand the role of big data in various sectors such as weather forecasting, healthcare, media and entertainment, logistics, travel and tourism, etc.
Conclusion: Advanced AI skillsets will take healthcare to new heights
If you’re looking to improve your team’s skill sets in healthcare research, product development, or healthcare services, AI and big data can help shape your strategy. Training AI engineers, machine learning experts, and big data engineers can go a long way as individuals try to find the right niche. Adding these skillsets can help prepare you and your employees for the rigors of the bold new world of global healthcare.
