The 8 Most Comforting Uses of AI in Healthcare Examples

Healthcare will change as a result of artificial intelligence, which is now the most powerful technology trend. It is already here in some places, enhancing the diagnostic abilities of dermatologists and radiologists, assisting emergency room triage choices, searching for potential new drug candidates, or enabling locked-in people to connect with others.

But this is only the start. A technological and cultural revolution is rapidly approaching. How will it affect us? How will it change how medicine is practiced? Our most downloaded e-book, A Guide To Artificial Intelligence In Healthcare, covers this subject. As always, the most recent update seeks to bring you up to speed and provides a comprehensive summary of what to expect and how to adjust to it.

I chose 8 fascinating examples of algorithms that are assisting healthcare professionals for this article, showing applications that are already being used in clinical settings and adding value for both medical professionals and patients.

1. Artificial intelligence can help with atrial fibrillation early detection

One of the disorders that can raise the risk of stroke, heart failure, and other heart-related issues is atrial fibrillation (AFib). Until recently, managing AFib was extremely challenging because it requires ongoing electrocardiogram (ECG) monitoring to provide information about heart rate and rhythm.

With the advent of digital health gadgets, that has altered. For instance, AliveCor's Kardia is an FDA-approved, medical-grade ECG recorder; the most recent model is practically credit card-sized; read our review for more information. It can inform you in under a minute if your ECG is normal, if you might have AFib, or if you encounter any "unclassified" dangers.

The Kardia algorithm virtually runs discreetly in the background while continuously analyzing readings. However, the business claims that it is "the most advanced A.I. ever introduced to personal ECG." By enabling a far larger range of cardiac diseases to be determined on a personal level, As a result of enabling the diagnosis of a considerably wider spectrum of cardiac diseases using a personal ECG device, "this suite of algorithms and visualizations will provide the framework for delivery of additional consumer and professional service offerings beyond AFib."

2.Algorithms for skin inspection support dermatologists

Users using skin-checking programs can snap pictures of their suspected skin lesions, submit them to a server, and have the images analyzed by an A.I. algorithm. A dermatologist will then validate the results.

In just a few seconds, these algorithms may generate a preliminary diagnostic by comparing user photos to the enormous database operating in the background.

We could even draw the conclusion that if such a system offers follow-up and access to doctors and therapies should the need arise, they are rather near to the ideal arrangement - they screen out non-existent situations and free dermatologists to concentrate on the real problems.

3.Using retinal pictures, AI diagnoses diabetic retinopathy on its own

The growing global burden of diabetic retinopathy (DR) screening can be alleviated by using artificial intelligence (AI) screening algorithms. When tested against their own internal datasets, many AI algorithms have been proven to perform as well as or better than human specialists at DR classification tasks.

This study used a sizable dataset gathered from two Veterans Affairs (VA) hospitals to validate seven commercially available DR screening algorithms in order to analyze how accurately these algorithms perform. Researchers compared the performance of seven commercially available algorithms, two of which have FDA authorization, and discovered substantial variances.

Their findings highlight the fact that, despite the fact that automated diabetic retinopathy (DR) screening devices can significantly increase access, they can not substitute for regular eye exams. The sole diagnosis of referrable DR utilizing current commercial DR screening systems' certified devices and methods Using automated screening techniques exclusively could result in the loss of additional significant traits such undetected glaucoma, macular degeneration, retinal detachments, or choroidal melanomas. To increase screening accessibility and keep a good standard of care, DR screening devices should be used in addition to conventional eye exams.

4. Skin-checking algorithms assist dermatologists in their work

Users using skin-checking programs can snap pictures of their suspected skin lesions, submit them to a server, and have the images analyzed by an A.I. algorithm. A dermatologist will then validate the results.

In just a few seconds, these algorithms may generate a preliminary diagnostic by comparing user photos to the enormous database operating in the background.

We could even draw the conclusion that if such a system offers follow-up and access to doctors and therapies should the need arise, they are rather near to the ideal arrangement - they screen out non-existent situations and free dermatologists to concentrate on the real problems.

5. AI can recognize stroke on CT scans and aid clinicians in beating the clock

In a standalone or multi-hospital network, Viz.ai's flagship product, Viz LVO, uses artificial intelligence to automatically identify suspected large vessel occlusion (LVO) strokes on computed tomography angiography (CTA) imaging and to immediately alert on-call stroke specialists about potentially treatable patients.

When the Southeast Regional Stroke Center at Erlanger began utilizing the algorithm in 2018, the first significant news about the algorithm broke. Since then, a lot more healthcare organizations have done the same. According to a recent large-scale real-world multi-center investigation employing Viz.ai, the median time-to-notification for all of the sites included when using Viz LVO was five minutes and 45 seconds.

In the study, which used scanners from various manufacturers and contained the largest health A.I. data set to date, Viz LVO identified LVOs in 2,544 consecutive patients from 139 hospitals with 96% sensitivity and 94% specificity. Viz LVO's faster triage makes it possible to treat more patients who qualify for thrombectomy, improving patient outcomes and lowering the likelihood of long-term impairment.

6.Pediatric seizure-detecting smart bands

Following migraine, stroke, and Alzheimer's disease in terms of frequency of occurrence, epilepsy is the fourth most prevalent neurological condition in the US.

Wearable gadgets, like Empatica's Embrace wristbands, can alert the user and/or loved ones and caregivers to a seizure or the possibility of one.

According to the manufacturer, Embrace's clinical testing on 141 individuals with epilepsy, including 80 children, showed a 98% accuracy rate for identifying generalized tonic-clonic seizures.

7. A.I. helps pathologists identify metastatic breast cancer

Breast cancer is the most prevalent cancer diagnosis for women. According to the latest Global Cancer (GLOBOCAN) statistics from the World Health Organization (WHO), accounting for 11.7% of the incidence and 15.5% of the mortality, ranking first among all cancers.

Deep learning models aiming to find early signs of the disease are around for a good number of years. Although we have not yet arrived at an omnipotent solution as of yet, studies show that combining deep learning systems’ predictions with human pathologists’ diagnoses improves patient outcomes.

There is still a long way to go, as this study points out “DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further research.”

8. A.I. builds complex and consolidated platforms for drug discovery

Time and efficiency are key in the operation of the pharmaceutical supply chain. Its main objective is to deliver the right medication to the person in need as fast as possible – to aid the healing process in the best way possible. While the drug designing, manufacturing, and distribution supply chains have been changing constantly due to new technologies, the scope and quality of the recent transformation are much more profound.