One of the fastest-growing parts of the economy in the last ten years has been healthcare, and in light of the growing threats of pandemics like the coronavirus outbreak, the industry is set to rise once again. To stay ahead of the curve in demand for healthcare services and solutions, organizations worldwide are turning to advanced techniques like AI, machine learning, and Big Data.
AI is going to be huge in healthcare. According to Acumen Research and Consulting, the global market will hit $8 billion by 2026 and there is a huge overlap of skills in AI and big data—where the processing of information is optimized to help solve business and real-world problems. AI and big data provide numerous potential benefits for individuals and companies alike, including:
- Empowering patient self-service with chatbots
- Diagnosing patients with faster computer-aided design
- Analyzing image data to examine the molecular structure in drug discovery, and by radiologists to analyze and diagnose patients
- Personalizing treatments with more insightful clinical data
Let’s take a look at a few examples of AI and big data at work in the healthcare sector.
How AI Can Predict Heart Attacks
Plaque is made of substances that circulate in the bloodstream, including cholesterol and fat. Over time, plaque builds up in arteries, causing them to narrow and stiffen. Similar to how sink drains can become clogged by food and debris, arteries can become clogged by 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 arteries is visible in CTA images, but measuring the amount of plaque can take an expert 25-30 minutes. So researchers at Cedars Sinai developed an AI algorithm that enables a computer to perform the same task in mere seconds.
The researchers fed a computer 900 coronary CTA images that had already been analyzed by experts. In this way, the computer “learned” how to identify and quantify plaque in the images. The AI algorithm’s measurements accurately predicted the incidence of heart attack within five years for 1611 people who participated in a related research trial.
AI in Preventative Health Care
The potential applications of AI in preventative health care are wide-ranging and profound. Beyond heart attacks, researchers are actively studying the use of AI to predict a multitude of other diseases and illnesses. For example:
- AI may help identify which currently healthy people are likely to develop breast cancer within five years, based on information hidden in mammograms that today’s clinicians can’t yet interpret
- AI can predict which patients in memory care clinics are likely to develop dementia within two years
- AI can aid early detection of diabetes by identifying hidden patterns of correlation in large patient data sets
- AI may assist in predicting acute kidney injury up to 48 hours before it occurs
In addition, AI is already being 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, lab results, vital sign recordings, and medication logs. AI algorithms can help doctors and nurses identify patterns of data that alert them to a change in patient status or risk of developing a serious complication. For example, AI can assist in:
- Early identification of sepsis, a life-threatening condition that occurs when the body’s immune system has an extreme response to infection
- Identifying fetuses in distress, using data from fetal heart monitors
- Alerting clinicians to when patients on mechanical ventilators need adjustment
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AI for In-Patient Mobility Monitoring
The clinical staff are busy people. Take intensive care unit (ICU) nurses, for example, who often have multiple patients in critical condition under their watch. Limited mobility and cognition during long-term treatments can adversely affect the patients’ overall recovery. Monitoring their activity is vital. To improve outcomes, researchers at Stanford University and Intermountain LDS Hospital installed depth sensors equipped with ML algorithms in patients’ rooms to keep track of their mobility. The technology accurately identified movements 87 percent of the time. Eventually, the 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 now, it can take up to 15 years to bring a new – and potentially life-saving – a drug to market, according to a report published in Trends in Pharmacological Sciences. It can also cost between $1.5 and $2 billion. Around half of that time is spent in clinical trials, many of which fail. Using AI technology, however, researchers can identify the right patients to participate in the experiments. Further, 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 undoubtedly cumbersome EHR systems will come up. Traditionally, clinicians would manually write down or type observations and patient information, and no two did it the same. Often, they would do it after the patient visit, inviting human error. With AI- and deep learning-backed speech recognition technology, however, interactions with patients, clinical diagnoses, and potential treatments can be augmented and documented more accurately and in near real-time.
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Physical Robots Use AI Too
Robots (the physical kind) are being used today in many types of businesses, such as in manufacturing and warehousing. But, robots are increasingly being used in hospitals as well, and many are designed to leverage AI. The National Center for Biotechnology Information (NCBI) reported that physical robots are becoming more collaborative with humans and can be trained to perform various tasks empowered by AI logic. And it’s not just delivering supplies in hospitals. Surgical robots can “provide ‘superpowers’ to surgeons, improving their ability to see and create precise and minimally invasive incisions, stitch wounds, and so forth.” With AI driving their decision-making processes, 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 groups (unlike “public health,” which focuses on how society ensures more healthy people). Big data is a massive part of this effort. A recent article in BuiltIn highlighted various companies that are leveraging big data to help healthcare organizations and researchers read the trends to improve health conditions.
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For example, one company called Linguamatics in Cambridge, MA uses natural language processing to mine through unstructured patient data to detect relevant lifestyle factors and predict which patients may be at higher risk for disease. Another company in Santa Clara, CA, called Hortonworks, helps organize and integrate billions of records so that pharmaceutical companies can do better research for clinical trials, raise the level of safety, and get products to market faster.
How Big Data Can Fight Cancer
Big data technologies are also being used in the battle against cancer. As reported in National Geographic, big data technologies can process clinical data to reveal hidden patterns that result in earlier diagnosis of cancer. The earlier it’s detected, the better the chances are for treating it. Big data technologies are adept at analyzing genome sequencing to identify biomarkers for cancer, and can also reveal groups that are at particular risk for cancer and find otherwise undiscovered treatments. The most progressive companies are using big data techniques to speed their analyses and create treatments faster and with more tangible results.
Challenges of AI in Health Care
The use of AI technology in health care is exciting, but not without its challenges. AI algorithms rely on identifying patterns in vast quantities of data. If the data is biased, inaccurate, not representative of a patient population, or compromised in any way, the conclusions based on them will also be flawed. In addition, even after new AI-powered clinical tools are fully vetted, it can be a long process to get them approved by the FDA, adopted by hospitals, and accepted by insurance companies.
AI-powered health care initiatives also need to be mindful of ethical concerns surrounding the mining of patient data. While AI-applications can be useful in predicting patient behavior (like who is likely to miss appointments, skip screenings, or refuse treatments), they need to do so in a way that preserves patient privacy and medical information.
Check out the video below to understand the role of big data in various sectors such as weather forecast, healthcare, media and entertainment, logistics, travel and tourism and more.
Conclusion: Advanced AI Skillsets are Driving Healthcare to New Heights
Whether you’re looking to improve team skillsets in healthcare research, product development or healthcare services, AI, and big data are helping to shape your strategy. Training for AI engineers, machine learning experts, and big data engineers can make a difference as individuals try to find the right niche. Adding these skillsets will be instrumental in preparing you or your workforce for the rigors of a bold new world of global healthcare.