Healthcare hirers, jobseekers: Know your healthcare AI

Among AI’s most watchful stakeholders are healthcare organizations in need of AI talent and AI talent in need of work in healthcare. Both groups need to keep up with the technology in its present as well as future iterations.

Fortunately for both, there’s no shortage of informational end educational resources to help with the assignment. 

The online learning platform Simplilearn has posted a fresh set. The article seems intended to coax readers into enrolling in a paid Simplilearn course or two, but it has value of its own. 

The piece presents a handful of use cases for healthcare AI. Here are excerpts from five of these. 

1. AI for inpatient mobility monitoring. 

In the ICU, limited mobility and cognition during long-term treatments can adversely affect patients’ overall recovery. It’s vital to monitor their movements, the authors point out. “To improve outcomes,” they write, “researchers at Stanford University and Intermountain LDS Hospital installed depth sensors equipped with machine learning algorithms in patients’ rooms to keep track of their mobility.” 

‘The technology accurately identified movements 87% of the time. Eventually, the researchers aim to provide ICU staff with notifications when patients are in trouble.’

2. AI for clinical drug trials. 

As things stand now, it can take up to 15 years to bring a new—and potentially lifesaving—drug to market, according to a report published in Trends in Pharmacological Sciences. “It can also cost between $1.5 and $2 billion,” Simplilearn reports. “Around half of that time is spent in clinical trials, many of which fail.” 

‘Using AI technology, pharma 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.’

3. AI for EHR quality improvement. 

“Ask any healthcare professional what the bane of their existence is, and undoubtedly cumbersome electronic medical records systems will come up,” the authors write. “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, interactions with patients, clinical diagnoses and potential treatments can be augmented and documented more accurately and in near real-time.’

4. AI for robot augmentation.  

Robots—the physical kind—are increasingly used in hospitals. Many are designed to leverage AI. “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.’  

5. AI for the war on cancer.  

Big data technologies are “adept at analyzing genome sequencing to identify biomarkers for cancer,” the authors note. “They 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.’

Addressing healthcare employers directly, Simplilearn writes:

‘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.’

The piece closes by listing a number of online courses on offer form Simplilearn. These range in duration and prices from “AI Engineer” (11 months, $1,449) to “AI & Machine Learning Bootcamp” (24 weeks, $8,000). 

Read the rest.