AI in healthcare: Most promising applications

Learn Jun 23, 2022

Have you wondered how much easier our lifestyles and daily chores are vs. those of the previous generation? Accept it, be they voice assistants, social media recommendation systems, maps, or navigation, they do spark our curiosity as to how? The question often remains, how exactly do they operate? To which there’s one simple answer: AI.

ai in healthcare

AI in healthcare is pretty much a blessing to the industry these days. Ever since the first attempts at AI and healthcare symbiosis, AI has progressed way beyond cutting down the time spent on the paperwork (the traditional healthcare record hassle). Enhanced operational productivity is just one area of benefit; meanwhile, AI shaking off patient outcomes is a whole new dimension that especially surged forward during the last decade. We’ll dive into that and more in a bit:

The merge of AI and healthcare

What we often assume by AI in healthcare is a broader implication: the use of algorithms and models that allow healthcare professionals to identify diseases, prescribe diagnoses, and make informed data-backed medical choices faster and more efficiently.

Driven by healthcare data availability and quick development of analytics systems, AI essentially replicates rational functions of humans-in-the-loop to the service of the very same humans. By studying snapshots of patient bodies, clinical inquiry trials, and documentation, a model then comes up with better preventive care recommendations. Though as exciting as it sounds in theory, the use of AI in healthcare evokes an alarming question:

“Will AI healthcare applications substitute doctors in the near future?”

They won’t. Instead, they will act as a support tool for doctors and healthcare professionals. With the right usage of AI technology, healthcare systems can access important information often neglected because of the large volumes of data. Where does that put us in terms of AI’s ultimate impact?

One way or another, AI will foster effective decision-making and will continue proposing better alternatives to existing problems, taking some load off of healthcare professionals’ backs.

AI applications in healthcare

By now, you can imagine the limitless variety of AI applications in healthcare, but the ones we will be focusing in this article are as follows:

Colonoscopy and gastrointestinal endoscopy

Before the implementation of AI, the common way to stop colorectal cancer was through the detection of a colorectal polyp during colonoscopy. And because neglected polyps can become cancerous if given enough time to grow, the development of AI-assisted colonoscopy that would screen, detect, and categorize polyps by size, brought up an unprecedented value to the medicine.

As AI has already demonstrated a significant decrease in the miss rate of polyp detection in research settings, the incidence rate is estimated to go down if properly integrated into real-life clinical scenarios. AI can also enhance the quality of gastrointestinal endoscopy by providing real-time inspection of the colonic mucosa and relevant insights to the endoscopist.

Dermatology

Albeit the widespread use of AI across the industry, its implementation in dermatology is comparatively limited yet. The challenge is in the amount of available clinical data that is representative of all skin types. Once this is achieved, AI can boost the sensitivity and accuracy of skin condition analysis, including acne, scaliness, hives, lesions, eczema, seborrheic dermatitis, and much more.

ai in dermatology

Dentistry

AI is used to analyze dental data and recognize a wide range of dental conditions. By applying classification and segmentation techniques, the technology makes it possible to make dental restoration legible to a model, by also spotting out the teeth that are subject to treatment.

Pathology and cancer cell detection

The universal shortage of pathologists was one of the main reasons for the inability to preserve accurate pathological analysis. The combination of the whole-slide imaging (WSI) scanner and AI technologies has assisted with the shortage issue and gene mutation calculation.

pathology and cancer cell detection

The ability of AI to leverage colossal quantities of data generated throughout the patient care lifecycle makes it possible to improve pathologic diagnosis, classification, prediction, and disease prognosis, in general. So, AI has become a great asset in analyzing scanned and microscopic images to identify cancer cells, take on cancer prevention measures, etc.

Radiology

Similar to pathology, there has been an unbalanced rate between trained radiologists and radiological imaging data. And with the rise of AI technologies in healthcare, the problem has become relatively minor.

Yet it did not stop there: AI in radiology has led to the advancement of the analysis of radiology images, including CT, brain and breast MRI scans, and other image modalities.

Robotic surgery

When it comes to speed, AI has revolutionized the concept of surgeries. Robot-assisted surgeries perform even complex surgeries with more precision and control as well as minimal incision. Since robots cannot get physically tired, the issue of exhaustion during important surgeries is out of the picture now, no matter how long the surgery takes.

All of this is doable when AI gets to iterate and learn based on the data from older operations to cultivate new approaches. When introducing quality training data, the model delivers outcomes that eliminate human error during the surgical procedure. Check out SuperAnnotate’s platform to ​​annotate, version, and manage ground-truth data for AI applications in medicine.

robotic surgery

Other beneficial aspects of AI

With the applications mentioned above, we do get a sense of the magnitude of AI in healthcare. Now, let’s take a look at some of its broader beneficial uses:

Conveying intelligence to medical devices

Smart devices are essential for checking on intensive care unit patients, and using AI can improve the identification of weakening. It can also assist in recognizing reporting on potential complications and lower the costs of hospital-acquired circumstance fines in the long run.

conveying intelligence to medical devices

Brain-computer interfaces

For a long time now, humans have been using computers as a means of communication, yet directly transforming what’s going on in the human mind into technology without the monitors and keyboards is fairly recent.

As nervous system traumas and neural diseases can restrict one’s ability to communicate with others verbally, brain-computer interfaces (BCIs) backed by AI can assist in such matters and offer the necessary skills to engage with other people with ease.

Bonus: Elon Musk’s Neuralink

We’re still in the early stages of brain-computer interface development, though the industry’s evolvement is fairly fast-paced. Elon Musk’s Neuralink is one of the pioneers in the field that has successfully implanted AI microchips in the brains of a monkey and a pig and is going to launch clinical trials in humans. Neuralink’s device will come in aid of those with neurological disorders, in cases where there’s a break off between the brain and the nerves that control bodily mocement. According to recent estimates, Neuralink will even cure tinnitus by 2027.

neuralink

Steps to encourage the implementation of AI in healthcare

Now that we’ve covered the benefits and use cases of AI in healthcare, let's turn to practicality. Not everything is as smooth as it may seem on the surface, and there are concrete steps we could take to encourage the implementation of ethical and responsible AI.

Population-representative data availability

AI-powered technology is trained on patient data, but what is often overlooked is the population-representative aspect. Once bias creeps in, forget about the model’s accuracy. And the lack of population-representative data is a form of bias. Accessing similar data is another challenge though, as it requires consent, manual collection, standardization, and much more.

Safety regulation

The only way to instill trust in medical technology among patients is by eliminating bias from data and having proper safety standards and regulations in place. Deployment is one thing but evaluating clinical AI in action is a whole other responsibility that overlaps with safety control.

AI-mature team

Further adoption of AI in healthcare will direct industry leaders through data science, biomedicine, etc. So, it is significant to conduct an AI-maturity level assessment first to identify the exact gaps within the team of doctors as immediate medical device users.

They will need all sorts of skills starting from digital literacy to machine learning and a continuous-learning mindset to leverage and have full control over AI-powered technology.

Investment in new talent

As mentioned earlier, with the applications of AI in healthcare, medical organizations have to think through the adoption of new roles and ways that will ensure the success of these new applications. This only highlights the need for organizations to attract the right talent that has an innovative mindset and is equipped to handle the ever-changing medical technology.

Key takeaways

Contrary to fairly popular belief or mistrust, the use of AI in health has already delivered numerous benefits. Healthcare organizations have achieved a balanced workflow by streamlining operations, automating patient record processes, and improving patient outcomes. With the help of AI-enabled devices, doctors can now carefully work out advanced rehabilitation programs and prescribe relevant treatment.

As groundbreaking as all these advancements seem, we should all be mindful that robots have been referred to in medical fields for only a few decades. The only difference now is that they have made headway into highly multifaceted surgical robots that assist human surgeons contrasting with old-fashioned laboratory robot prototypes.

And despite speculations that AI in healthcare will soon be replacing humans in the field as they now have the ability to do better than humans, it seems unlikely or too early to happen for a large number of medical jobs.

Connect with us to find out how SuperAnnotate can help you to build your next medical AI.

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