Artificial intelligence is no longer a futuristic idea. It's already here, and in healthcare it has
turned out to be a powerful, disruptive force, fueling some of the most innovative diagnostic
tools of today. So let’s take a look at 7 examples where AI has started to transform healthcare.
I’m Dr. Bertalan Meskó, and you’re watching The Medical Futurist.
1.) Detecting arrhythmias
Atrial fibrillation or aFib is a condition that can increase the risk of stroke,
heart failure, and other heart-related complications. But until recently,
it has been overwhelmingly difficult to deal with it,
as it needs continuous ECG monitoring to provide data about heart rate and rhythm.
But AI has already changed that with the appearance of digital health
devices. For example, AliveCor’s Kardia is an FDA-approved, medical-grade ECG recorder,
that’s “the most sophisticated A.I. ever brought to personal ECG” - according to the company.
The Kardia algorithm doesn’t just switch the point of care from the hospitals to wherever
the patient is but it runs pretty much invisibly in the background, analyzing readings on the go.
2.) Emergency Detection
AI has the potential to highly optimise patient pathways and even benefit nurses too. HCA
Healthcare developed a predictive algorithm called Sepsis Prediction and Optimisation of
Therapy. It can continuously monitor patient data in hospitals to identify potentially
impending sepsis cases. The algorithm is able to detect sepsis six hours earlier than clinicians,
enabling the healthcare system to cut sepsis mortality rates by nearly 30%.
3.) Seizure-detecting smart bracelets
Epilepsy is the 4th most common neurological problem in the US,
following migraine, stroke and Alzheimer’s disease. But wearable devices, like the
Empatica’s AI fueled wristband are designed to notify users and caretakers about a developing
seizure. Clinical testing of Embrace yielded a 98% accuracy rate for detecting generalised
tonic-clonic seizures. The long term plan of course is to be able to predict seizures too.
4.) Skin-checking apps
Skin-checking applications allow users to take pictures of their suspicious skin lesions,
upload these pictures, and get the images evaluated by an A.I. algorithm.
These algorithms work by comparing user images to vast databases,
coming up with a preliminary diagnosis in just a few seconds. While dermatologists are still
absolutely required for confirmed diagnoses, these applications are highly accurate and
can detect skin lesions that could’ve been missed by the traditional healthcare process.
5.) Stroke Detection
The Viz LVO app uses deep learning algorithms to automatically detect stroke on CT imaging
and alert on-call stroke specialists about potentially treatable patients.
Since stroke is one of the most time-sensitive conditions in all medicine, this system is a
game changer by cutting down several hours for patients to receive the care they need.
In a study, containing the largest health A.I. dataset to date,
Viz LVO achieved 96% sensitivity and 94% specificity. Faster triage with this
application enables the identification and treatment of more patients which
improves patient outcomes and reduces the chances of long-term disability.
6.) Breast cancer detection
Breast cancer is the most prevalent cancer diagnosis for women. But deep
learning models aiming to find early signs of the disease have been around for a good
number of years now. Although we have not yet arrived at an all-encompassing
solution yet, studies show that combining deep learning systems’ predictions with
human pathologists’ diagnoses improves patient outcomes and alleviates at least some of the
burden on radiologists when they need to make life-altering decisions for their patients.
7.) Drug discovery
Advanced algorithms could fundamentally alter the traditional process of designing drugs
too. AI could make drug development much cheaper and more effective by
shortening the drug production cycle, and helping out the pharmaceutical industry
in finding new drugs without burdening clinical trials and accumulating costs.
According to some estimates, it takes about 12 years and close to 3 billion dollars for
one single experimental drug to advance from concept to market. One. Recently,
an advanced machine-learning program from Alphabet’s DeepMind has revealed 200 million
proteins. This could speed the search for new medicines. 200 million! That’s the difference
A.I. can make. And examples like this are popping up everywhere in the healthcare landscape now,
profoundly changing the way we can bring new solutions to our maladies.
Of course, these are just a few exciting examples of how artificial intelligence
can fuel the capabilities of healthcare professionals. There is still a long way to
go and figure out how to further implement these algorithms, not only from the technological point
of view but also in terms of regulations. But what’s clear is that AI already means
a paradigm shift for medical professionals and patients alike, and it only just started.