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Artificial Intelligence has been promising to revolutionize healthcare for quite some time; however, one look at any modern hospital or healthcare facility, and it is easy to see that the revolution is already here.

In almost every patient touchpoint AI is already having an enormous impact on changing the way healthcare is delivered, streamlining operations, improving diagnostics, and improving outcomes.

Although the deployment of AI in the healthcare sector is still in its infancy, it is becoming a much more common sight. According to technology consulting firm, Gartner, healthcare IT spending for 2021 was a hefty $140 billion worldwide, with enterprises listing “AI and robotic process automation (RPA)” as their lead spending priorities.

Here, in no particular order or importance, are seven of the top areas where healthcare AI solutions are being developed and currently deployed.

1. Operations and Administration
A hospital’s operation and administration expenses can be a major drain on the healthcare system. AI is already providing tools and solutions that are designed to improve and streamline administration. Such AI algorithms are proving to be invaluable for insurers, payers, and providers alike. Specifically, there are several AI programs and AI healthcare startups that are dedicated to finding and eliminating fraud. It has been estimated that healthcare fraud costs insurers anywhere between $70 billion and $234 billion each year, harming both patients and taxpayers.

2. Medical Research
Probably one of the most promising areas where AI is making a major difference in healthcare is in medical research. AI tools and software solutions are making an astounding impact on streamlining every aspect of medical research, from improved screening of candidates for clinical trials, to targeted molecules in drug discovery, to the development of “organs on a chip” – AI combined with the power of ever-improving Natural Language Processing (NLP) is changing the very nature of medical research for the better.

3. Predictive Outcomes and Resource Allocation
AI is being used in hospital settings to better predict patient outcomes and more efficiently allocate resources. This proved extraordinarily helpful during the peak of the pandemic when facilities were able to use AI algorithms to predict upon admission to the ER, which patients would most benefit from ventilators, which were in very short supply. Similarly, a Stanford University pilot project is using AI algorithms to determine which patients are at high risk of requiring ICU care within an 18 to 24 hours period.

4. Diagnostics
AI applications in diagnostics, particularly in the field of medical imaging, are extraordinary. AI can “see” details in MRIs and other medical images far greater than the human eye and, when tied into the enormous volume of medical image databases, can make far more accurate diagnoses of conditions such as breast cancer, eye disease, heart, and lung disease and so much more. AI can look at vast numbers of medical images and then identify patterns in seconds that would take human technicians hours or days to do. AI can also detect minor variations that humans simply could not find, no matter how much time they had. This not only improves patient outcomes but also saves money. For example, studies have found that earlier diagnosis and treatment of most cancers can cut treatment costs by more than 50%.

5. Training
AI is allowing medical students and doctors “hands-on training” via virtual surgeries and other procedures that can provide real-time feedback on success and failure. Such AI-based training programs allow students to learn techniques in safe environments and receive immediate critique on their performance before they get anywhere near a patient. One study found that med students learned skills 2.6 times faster and performed 36% better than those not taught with AI.

6. Telemedicine
Telemedicine has revolutionized patient care, particularly since the pandemic, and now AI is taking remote medicine to a whole new level where patients can tie AI-driven diagnostic tools through their smartphones and provide remote images and monitoring of changes in detectable skin cancers, eye conditions, dental conditions and more. AI programs are also being used to remotely monitor heart patients, diabetes patients, and others with chronic conditions and help to ensure they are complying with taking their medications.

7. Direct treatment
In addition to adding better clinical outcomes with improved diagnostics and resource allocation, AI is already making a huge difference in the direct delivery of treatments. One exciting and extremely profound example of this is robotic/AI-driven surgical procedures. Minimally invasive and non-invasive AI-guided surgical procedures are already becoming quite common. Soon, all but some of the most major surgeries, such as open heart surgeries, can and will be done as minimally invasive procedures, and even the most complex “open procedures” will be made safer, more accurate, and more efficient thanks to surgical AI and digital twins of major organs such as lungs and the heart.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.
BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

NLP evolved to be an important way to track and categorize viewership in the age of cookie-less ad targeting. While users resist being identified by a single user ID, they are much less sensitive to and even welcome the chance for advertisers to personalize media content based on discovered preferences. This personalization comes from improvements made upon the original LDA algorithm and incorporate word2vec concepts.

The classic LDA algorithm developed at Columbia University raised industry-wide interest in computerized understanding of documents. It incidentally also launched variational inference as a major research direction in Bayesian modeling. The ability of LDA to process massive amounts of documents, extract their main theme based on a manageable set of topics and compute with relative high efficiency (compared to the more traditional Monte Carlo methods which sometimes run for months) made LDA the de facto standard in document classification.

However, the original LDA approach left the door open on certain desirable properties. It is, at the end, fundamentally just a word counting technique. Consider these two statements:

“His next idea will be the breakthrough the industry has been waiting for.”

“He is praying that his next idea will be the breakthrough the industry has been waiting for.”

After removal of common stop words, these two semantically opposite sentences have almost identical word count features. It would be unreasonable to expect a classifier to tell them apart if that’s all you provide it as inputs.

The latest advances in the field improve upon the original algorithm on several fronts. Many of them incorporate the word2vec concept where an embedded vector is used to represent each word in a way that reflects its semantic meaning. E.g. king – man + woman = queen

Autoencoder variational inference (AVITM) speeds up inference on new documents that are not part of the training set. It’s variant prodLDA uses product of experts to achieve higher topic coherence. Topic-based classification can potentially perform better as a result.

Doc2vec – generates semantically meaningful vectors to represent a paragraph or entire document in a word order preserving manner.

LDA2vec – derives embedded vectors for the entire document in the same semantic space as the word vectors.

Both Doc2vec and LDA2vec provide document vectors ideal for classification applications.

All these new techniques achieve scalability using either GPU or parallel computing. Although research results demonstrate a significant improvement in topic coherence, many investigators now choose to deemphasize topic distribution as the means of document interpretation. Instead, the unique numerical representation of the individual documents became the primary concern when it comes to classification accuracy. The derived topics are often treated as simply intermediate factors, not unlike the filtered partial image features in a convolutional neural network.

With all this talk of the bright future of Artificial Intelligence (AI), it’s no surprise that almost every industry is looking into how they will reap the benefits from the forthcoming (dare I say already existing?) AI technologies. For some, AI will merely enhance the technologies already being used. For others, AI is becoming a crucial component to keeping the industry alive. Healthcare is one such industry.

The Problem: Diminishing Labor Force

Part of the need for AI-based Healthcare stems from the concern that one-third of nurses are baby boomers, who will retire by 2030, taking their knowledge with them. This drastic shortage in healthcare workers poses the imminent need for replacements and, while the enrollment numbers in nursing school stay stable, the demand for experienced workers will continue to increase. This need for additional clinical support is one area where AI comes into play. In fact, these emerging technologies will not only help serve as a multiplier force for experienced nurses, but for doctors and clinical staff support as well.

Healthcare-AI Automation Applications to the Rescue

One of the most notable solutions for this shortage will be automating processes for determining whether or not a patient actually needs to visit a doctor in-person. Doctors’ offices are currently inundated with appointments and patients who’s lower-level questions and concerns could be addressed without a face-to-face consultation via mobile applications. Usually in the from of chatbots, these AI-powered applications can provide basic healthcare support by “bringing the doctor to the patient” and alleviating the need for the patient to leave the comfort of their home, let alone scheduling an appointment to go in-office and visit a doctor (saving time and resources for all parties involved).

Should a patient need to see a doctor,  these applications also contain schedulers capable of determining appointment type, length, urgency, and available dates/times, foregoing the need for constant human-based clinical support and interaction. With these AI schedulers also comes AI-based Physician’s Assistants that provide additional in-office support like scheduling follow-up appointments, taking comprehensive notes for doctors, ordering specific prescriptions and lab testing, providing drug interaction information for current prescriptions, etc. And this is just one high-level AI-based Healthcare solution (albeit with many components).

With these advancements, Healthcare stands to gain significant ground with the help of domain-specific AI capabilities that were historically powered by humans. As a result, the next generation of healthcare has already begun, and it’s being revolutionized by AI.