Artificial Intelligence (AI) is benefiting healthcare organizations by implementing cognitive technology in order to unwind a huge amount of medical records and to perform any power diagnosis. This can majorly be of help in areas, where professionals aren’t available for medical emergencies. According to a report, artificial intelligence will be contributing at least 15.7 trillion to the world economy by 2030 and the greatest impact of it will be in the field of healthcare.

Now, why is that?

Two major reasons why AI is impactful in the field of healthcare are:

  1. High availability of medical data:

    We all have our medical histories noted down by doctors, in our accounts. Considering the population of a whole country, this data becomes huge. The implementation of Artificial Intelligence with the availability of such huge and useful data makes the process easier and efficient. AI is based on technologies such as deep learning and machine learning, whose algorithms work on test data, wherein they recognize patterns. While going through the medical records, they are likely to pick up relations amongst patients diagnosed with the same disease. With wearable tech devices, data records can be records and AI can help in diagnosing the disease pattern.

  2. The surge in advancements in ML:

    The medical data that is present is of very high dimension and consists of a lot of attributes and variables. In order to process and analyze this data, we needed complex algorithms that were introduced with the subsets of Machine Learning, deep learning, and neural networks. The strength of these algorithms lies in their ability to make sense out of complex, noisy, or nonlinear data. Neural networks can provide robust solutions to problems in a wide range of disciplines, particularly in areas where classification, prediction, optimization, pattern recognition, filtering, and function approximation are involved.

A couple of use cases of AI in healthcare

  1. Radiology:

    In 2018, Artificial Intelligence’s deep learning algorithm was programmed using 50,000 normal chest images and 7,000 scans with active TB. By working on them, it was able to predict results better than the radiologists themselves.

  2. Dermatology:

    Researchers used 100,000 images of malignant melanomas to train a deep learning neural network algorithm to identify skin cancer. After its training, the results given by the algorithm were compared to the performance of 58 international dermatologists. While the dermatologists were able to detect more than 86% of the melanomas, the algorithm detected 95% of them!

  3. MRI:

    A team of researchers at MIT developed a neural network called ‘Voxel Morph’, which was based on the concept of neural networks. Morph was trained on a dataset of approximately 7,000 MRI scans. When tested, it took seconds to perform the MRI analysis that took about four-five hours by the conventional MRI program.

These new studies still need to be validated and repeated on more people before they could gain broader acceptance. This could still be the opening of new doors in the healthcare industry.

Applications in the healthcare sector:

  1. Detecting diseases at an earlier stage:

    We have all heard of ‘Wearable-tech devices’, that track data like a person’s heart rate, sleep cycle, breathing rate, activity level, blood pressure, and so on and it keeps a record of all of these measures 24 by 7. Now all this data is fed to the ML algorithms to make predictions and display any warnings when the device collides something unusual or something unlikely. Using ML and deep learning algorithms we can build a model that predicts the risk of a heart attack and thus, help in predicting whether a person has chances of getting a heart attack or not. These warnings are given when there is an increase in blood pressure or heart rate.

  2. Medical assistants:

    Virtual Nursing Assistant is a virtual nurse, which provides you with clinical advice like allowable food and necessary medication. Also, it can help people with Alzheimer’s disease with their appointment schedules, and self-care tips. Here we can see the implementation of natural language processing, speech recognition, machine learning, and wireless integration with medical devices such as blood pressure cuffs in order to provide medical assistance to the patient.

  3. Decision Making in Surgeries:

    Robots can use Artificial Intelligence to use data from past operations to inform new surgical techniques. The ‘Da Vinci Surgical System’ is a robotic surgical system, designed to facilitate surgery using a minimally invasive approach and is controlled by a surgeon from a console. It helps in aiding the surgeons with an advanced set of instruments. Translating the surgeon’s hand moments at the console also produces clear and magnified images of the surgical area in real-time.

Currently, AI in robots is used for helping the surgeon in the decision making, but in the near future, we can expect to have robots perform surgeries, and that too more precisely than human surgeons.

Artificial Intelligence for Administrators

  1. Application of algorithms to Data: Algorithms are put to use to derive useful insights from unstructured data which helps in customer retention. To further explain the process, we’ll take an example of the Nuance Software. ‘Nuance’ is a production service provider company that uses artificial intelligence in managing, analysing, and reformatting patient’s data and/or healthcare history records. This makes the data more accessible and useful at the right moment.
    Key features of Nuance:
    Service acceleration:

    This step is where the software derives insights to suggest the best next step to be taken, in order to improve customer retention.

    Deflection:

    It anticipates the customer’s intent. If not found relatable, diverts the customer to other online engagements. This minimizes the volume of inbound calls and saves cost.

    Churn reduction:

    By using Natural Language Processing and Machine Learning, it derives patterns like cancellations in services, in the behaviour of leads. This then helps in taking the necessary steps for customer retention.

So, basically by studying patterns and the behavioural trends of all the customers, here technology has helped us in automation of tedious tasks, helped in customer retention, and also boosted revenue generation.

Future Growth Areas:

Today not many people understand the gravity of the problem called ‘mental health’. We might have technology connecting the neocortex of our brain to the cloud and understand the neurochemistry and physiology of the brain and thus, understanding the mental sickness symptoms beforehand.

Early detection can prove to be a life-saver in the cases of patients diagnosed with Cardiovascular diseases too. Studies have shown that Eye can often be the manifestation centre of cardiovascular disease, data scientists are applying deep-learning methods to identify the risk factors like smoking status, age, and blood pressure only by looking in the eye.

CRISPR gene editing is a genetic engineering technique in molecular biology by which the genomes of living organisms may be modified. This technology will enable us to go in there, select a particular snippet of a genome. Then, select a code, insert it, replace it or add it in. Gene therapy could lead to anti-ageing therapies and produce a cure for HIV too.

We’re going from evolution by natural selection, which is ‘Darwinism’ to evolution by ‘Human Direction’.

-Says Peter H. Diamandis, founder, and chairman of the X Prize Foundation.

Conclusion

While some believe that in the pursuit of objectivity, science has lost its heart. And, AI will lead to a destructive future that would siphon jobs and replace humans. We believe that if used correctly, AI can be our biggest friend and growth accelerator.

In the healthcare sector, precision and knowledge are the most important preliminaries. AI is expected to raise the bar as well as help achieve both, in the coming times.