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While most of us get that Artificial Intelligence (AI) is no longer a thing of science fiction and that we interact with it daily — in many ways, it’s only just beginning. Many are understandably still wary of AI for ethics and privacy reasons or worry that machines will take their jobs. There already are some promising applications of AI in healthcare, however. PathAI, for instance, has developed machine and deep learning algorithms that help pathologists diagnose cancer more accurately. That’s something, yet, there are still significant challenges the industry is facing, both culturally and technically. First, let’s dig into some of the ways AI in healthcare can benefit the industry.
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Applying AI in Healthcare
Let’s not kid ourselves. AI is going to be huge in healthcare. According to Acumen Research and Consulting, the global market will hit $8 billion by 2026. Other tech giants like IBM, Oracle, and AMD already have industry-specific solutions, too. While there are dozens of ways organizations can harness AI in healthcare, let’s look at a few.
In-Patient Mobility Monitoring
The clinical staff is 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.
Industry Challenges Persist
While the potential benefits, AI, and machine learning bring to the healthcare table are quite clear, there are many challenges to overcome. Long-ingrained institutional practices and different cultures in organizations cannot be optimized by merely slapping an algorithm on them, after all. Legacy EHR and Electronic Medical Systems that run on-premises don’t necessarily play well with other organizations’ ones either. Organizations also need to consider strict government regulations that are always changing. Making sense of the sheer volumes of data being generated today — which is primarily unstructured — isn’t an easy thing to do, either. That’s why data scientists, trained in the latest technologies and techniques, are in such high demand in the healthcare industry today.
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